Results 111 to 120 of about 5,619,500 (318)

Recycling of Thermoplastics with Machine Learning: A Review

Advanced Functional Materials, EarlyView.
This review shows how machine learning is revolutionizing mechanical, chemical, and biological pathways, overcoming traditional challenges and optimizing sorting, efficiency, and quality. It provides a detailed analysis of effective feature engineering strategies and establishes a forward‐looking research agenda for a truly circular thermoplastic ...
Rodrigo Q. Albuquerque, Florian Stephan, Annalena Pongratz, Christian Brütting, Katharina Krause, Holger Ruckdäschel   +5 more
wiley   +1 more source

Learning in an invertebrate with two types of negative reinforcement [PDF]

, 1966
Joseph E. Morrow
openalex   +1 more source

The Synergy of Artificial Intelligence and 3D Bioprinting: Unlocking New Frontiers in Precision and Tissue Fabrication

Advanced Functional Materials, EarlyView.
Advances in integrating artificial intelligence into 3D bioprinting are systematically reviewed here. Machine learning, computer vision, robotics, natural language processing, and expert systems are examined for their roles in optimizing bioprinting parameters, real‐time monitoring, quality control, and predictive maintenance.
Joao Vitor Silva Robazzi, Irem Deniz Derman, Deepak Gupta, Logan Haugh, Yogendra Pratap Singh, Vaibhav Pal, Yasar Ozer Yilmaz, Suihong Liu, Andre Luis Dias, Rogerio Andrade Flauzino, Ibrahim Tarik Ozbolat   +10 more
wiley   +1 more source

Effect of intertrial reinforcement and nonreinforcement on reversal learning [PDF]

, 1967
James Bowen
openalex   +1 more source

Additively Manufactured 3D Auxetic Metamaterials for Structurally Guided Capacitive and Resistive Tactile Sensing

Advanced Functional Materials, EarlyView.
A 3D‐architected auxetic metamaterial is used to construct capacitive and resistive tactile sensors via digital light processing‐based additive manufacturing. The inward deformation of the proposed structure under compression amplifies local strain, enhancing sensing performance.
Mingyu Kang, Hong‐Gap Choi, Keun Park, Soonjae Pyo   +3 more
wiley   +1 more source

Probability learning in the goldfish: I. Aversive reinforcement [PDF]

, 1966
Forrest W. Young, Harman V.S. Peeke
openalex   +1 more source

Understanding Functional Materials at School

Advanced Functional Materials, EarlyView.
This review outlines strategies for effectively teaching nanoscience in schools, focusing on challenges such as scale comprehension and curriculum integration. Emphasizing inquiry‐based learning and chemistry core concepts, it showcases hands‐on activities, digital tools, and interdisciplinary approaches.
Johannes Claußnitzer, Jürgen Paul
wiley   +1 more source

Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture

Smart Agricultural Technology
Digital Twins have gained attention in various industries by creating virtual replicas of real-world systems through data collection and machine learning.
Georg Goldenits, Kevin Mallinger, Sebastian Raubitzek, Thomas Neubauer   +3 more
doaj   +1 more source

Learning with prolonged delay of reinforcement [PDF]

, 1966
John García, Frank R. Ervin, R. A. Koelling   +2 more
openalex   +1 more source

Accelerated Reinforcement Learning

2017 14th IEEE India Council International Conference (INDICON), 2017
Policy gradient methods are widely used in reinforcement learning algorithms to search for better policies in the parameterized policy space. They do gradient search in the policy space and are known to converge very slowly. Nesterov developed an accelerated gradient search algorithm for convex optimization problems. This has been recently extended for
openaire   +3 more sources