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Lab-scale termite damage synthesis using least squares generative adversarial networks
2020 - IRG/WP 20-20674
This manuscript investigated the feasibility of least squares generative adversarial networks (LSGAN) to generate synthetic images of lab-scale termite damage based on AWPA E1 standard, to push machine-learning forward into wood science field and to ameliorate the lack of a termite damage dataset. We leveraged LSGAN to learn the distribution of 203 uniquely termite damaged samples from previous ex...
D J V Lopes
A new approach to wood protection: Potential of biologically synthesised CuO and ZnO nanoparticle formulation as a wood preservative
2022 - IRG/WP 22-30758
Even though metal nanoparticles are effective in protecting wood, they are less preferred, as they are synthesised using methods which are expensive and are not environmentally friendly. Biological synthesis of nanoparticles using plants, fungi, yeast, bacteria and viruses, has been accepted as an alternative approach and is referred to as green synthesis. Utilization of biologically synthesised n...
Shiny K S, R Sundararaj, N Mamatha
Effect of enzymatic inhibitors on the population of Reticulitermes grassei and their associated microbiota
2023 - IRG/WP 23-11008
Termites are pests that invade urban, agricultural and forest environments worldwide. They are economically important owing to the damage they cause to wood, wood products, building materials, agricultural products and forests. There are many methods for its control, but many of them cause negative environmental effects due to their toxicity. In termites, dietary change or antibiotic treatment may...
S M Santos, M T Troya, F Llinares, P Colina, E García, M Álvarez, L Robertson