Prof. Stavros Avramidis
Response of hygroscopicity to heat treatment and its relation to durability of thermally modified wood
T Li, D Cheng, S Avramidis, MEP Wålinder, D Zhou
Construction and Building Materials 144, 671-676, 2017
Radio frequency vacuum drying of wood. I. Mathematical model
A Koumoutsakos, S Avramidis, SG Hatzikiriakos
Drying Technology 19 (1), 65-84, 2001
Radio frequency vacuum drying of wood. II. Experimental model evaluation
A Koumoutsakos, S Avramidis, SG Hatzikiriakos
Drying technology 19 (1), 85-98, 2001
Application of near-infrared spectroscopy for moisture-based sorting of green hem-fir timber
K Watanabe, SD Mansfield, S Avramidis
Journal of wood science 57, 288-294, 2011
Behaviour of solid wood and bound water as a function of moisture content. A proton magnetic resonance study
CD Araujo, S Avramidis, AL MacKay
Walter de Gruyter, Berlin/New York 48 (1), 69-74, 1994
The effect of resin content and face-to-core ratio on some properties of oriented strand board
S Avramidis, LA Smith
Holzforschung 43 (2), 131-133, 1989
Predicting wood thermal conductivity using artificial neural networks
S Avramidis, L Iliadis
Wood and Fiber Science, 682-690, 2005
Multiphysics modeling of vacuum drying of wood
S sandoval Torres, W Jomaa, JR Puiggali, S Avramidis
Applied Mathematical Modelling 35 (10), 5006-5016, 2011
Neural network prediction of bending strength and stiffness in western hemlock (Tsuga heterophylla Raf.)
SD Mansfield, L Iliadis, S Avramidis
Walter de Gruyter 61 (6), 707-716, 2007
Classification of thermally treated wood using machine learning techniques
V Nasir, S Nourian, S Avramidis, J Cool
Wood Science and Technology 53, 275-288, 2019
Prediction of timber kiln drying rates by neural networks
H Wu, S Avramidis
Drying Technology 24 (12), 1541-1545, 2006
On the permeability of main wood species in China
F Bao, J Lu, S Avramidis
Walter de Gruyter 53 (4), 350-354, 1999
Prediction of physical and mechanical properties of thermally modified wood based on color change evaluated by means of “group method of data handling”(GMDH) neural network
V Nasir, S Nourian, S Avramidis, J Cool
Holzforschung 73 (4), 381-392, 2019
The basics of sorption
S Avramidis
Proceedings of international conference of COST action E 8, 1-16, 1997
Drying characteristics of thick lumber in a laboratory radio-frequeocy/vacuum dryer
S Avramidis, F Liu
Drying technology 12 (8), 1963-1981, 1994
Analysis of the wood sorption isotherm using clustering theory
ID Hartley, S Avramidis
Walter de Gruyter, Berlin/New York 47 (2), 163-167, 1993
Non-destructive measurement of moisture distribution in wood during drying using digital X-ray microscopy
K Watanabe, Y Saito, S Avramidis, S Shida
Drying technology 26 (5), 590-595, 2008
Wood-water sorption isotherm prediction with artificial neural networks: a preliminary study
S Avramidis, L Iliadis
Walter de Gruyter 59 (3), 336-341, 2005
An irreversible thermodynamics model for unsteady-state nonisothermal moisture diffusion in wood
S Avramidis, SG Hatzikiriakos, JF Siau
Wood Science and Technology 28 (5), 349-358, 1994
On the loss factor of wood during radio frequency heating
B Zhou, S Avramidis
Wood science and technology 33 (4), 299-310, 1999
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