Intellectualization is an important direction of drying development and artificial intelligence (AI) technologies have been widely used to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in different food drying technologies due to the advantages of self-learning ability, adaptive ability, strong fault tolerance and high degree robustness to map the nonlinear structures of arbitrarily complex and dynamic phenomena. This article presents a comprehensive review on intelligent drying technologies and their applications. The paper starts with the introduction of basic theoretical knowledge of ANN, fuzzy logic and expert system. Then, we summarize the AI application of modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products in artificial biomimetic technology (electronic nose, computer vision) and different conventional drying technologies. Furthermore, opportunities and limitations of AI technique in drying are also outlined to provide more ideas for researchers in this area.

An intraoperative infusion of DEX reduced cumulative morphine consumption and adverse effects after craniotomy.

P enhanced Al resistance in the Al-resistant L. bicolor species but not in the Al-sensitive L. cuneata under relatively high Al stress, although P in L. cuneata might also possess an alleviative potential. Enhancement of Al resistance by P in the resistant species might be associated with its more efficient P accumulation and translocation to shoots and greater Al exclusion from root tips after P application, but not with an increased exudation of organic acids from roots.