Results 121 to 130 of about 5,876,040 (331)
Effects of incidental training and reinforcement on mixed schema learning [PDF]
, 1968 Ed M. Edmonds, Marvin R. Mueller
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Advanced Materials, EarlyView.A novel one‐shot integration electropolymerization (OSIEP) method is developed as a breakthrough on the intricate photolithographic steps, enabling to compress all processes from synthesis to channel integration in one‐shot manufacturing. The specially designed dual bipolar electrodes provide the targeted depositions of poly(3,4‐ethylenedioxythiophene)
Jiyun Lee +9 more
wiley +1 more source
Learning in an invertebrate with two types of negative reinforcement [PDF]
, 1966 Joseph E. Morrow
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Advancing from MOFs and COFs to Functional Macroscopic Porous Constructs
Advanced Materials, EarlyView.This review study investigates the recent progress and methodologies for manufacturing metal–organic framework (MOF) or covalent–organic framework (COF)‐based 3D structured macroscopic porous constructs with high structural integrity, providing the possibility to control their porosity across dimensions.
Seyyed Alireza Hashemi +8 more
wiley +1 more source
Probability learning in the goldfish: I. Aversive reinforcement [PDF]
, 1966 Forrest W. Young, Harman V.S. Peeke
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Clustering with Reinforcement Learning
, 2007 We show how a previously derived method of using reinforcement learning for supervised clustering of a data set can lead to a sub-optimal solution if the cluster prototypes are initialised to poor positions. We then develop three novel reward functions which show great promise in overcoming poor initialization. We illustrate the results on several data
Barbakh, Wesam, Fyfe, Colin
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Computational Modeling of Reticular Materials: The Past, the Present, and the Future
Advanced Materials, EarlyView.Reticular materials are advanced materials with applications in emerging technologies. A thorough understanding of material properties at operating conditions is critical to accelerate the deployment at an industrial scale. Herein, the status of computational modeling of reticular materials is reviewed, supplemented with topical examples highlighting ...
Wim Temmerman +3 more
wiley +1 more source
Conventional and reversed partial reinforcement effects in selective learning [PDF]
, 1964 William B. Pavlik, William F. Reynolds
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Hierarchically MOF‐Based Porous Monolith Composites for Atmospheric Water Harvesting
Advanced Materials, EarlyView.This review explores the design of hierarchical porous materials for atmospheric water harvesting, focusing on metal‐organic frameworks (MOFs) and porous monoliths. Emphasis is placed on integrating MOF nanoscale porosity with the microscale channels of monolithic scaffolds to enhance sorption‐desorption performance.
Mahyar Panahi‐Sarmad +7 more
wiley +1 more source
Direct reinforcement, sex of model, sex of subject, and learning by vicarious reinforcement [PDF]
, 1969 R. E. Phillips +2 more
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