Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intens...
Assessing the mutual benefits of artificial intelligence (AI) and bioenergy systems, to promote efficient and sustainable energy production. By addressing issues with conventional bioenergy techniques, it highlights how AI is revolutionising optimisa...
This study focuses on modelling sustainable concretes' mechanical and environmental properties with interpretable artificial intelligence-based automated rule extraction, management of waste materials, and meeting future prospects. In this context, 2...
A complex interplay of genetic and environmental factors influences bacterial growth. Understanding these interactions is crucial for insights into complex living systems. This study employs a data-driven approach to uncover the principles governing ...
Neural networks : the official journal of the International Neural Network Society
39705770
In this paper we present three neurocontrol problems where the analytic policy gradient via back-propagation through time is used to train a simulated agent to maximise a polynomial reward function in a simulated environment. If the environment inclu...
Artificial intelligence (AI) and its applications in digital health, bioengineering, and society have significant material impacts on the environment owing to AI's vast energy demands and energy consumption, carbon footprints, and water usage to cool...
In poultry farming, robotics is represented by robotic models which perform different functions. Among these functions, ambience is one of the most critical, as the aviary is an artificial environment which needs to present all the necessary conditio...
Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framewo...
Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underly...
In many applications, such as coverage exploration and search and rescue missions, accurately assessing environmental complexity is valuable for performance evaluation and algorithm adjustments. Despite this, in the context of multi-robot systems, qu...