原始名称记载: Purdue Research Foundation

This disclosure relates to cementitious systems that are useful in the chemical arts, such as in the manufacture of products, such as construction materials. In particular, the present disclosure relates to compositions and methods for preparing certain cementitious systems, such as compositions including a cementitious material, limestone, and cellulose nanofibers (CNFs).

This disclosure relates to compositions, materials, and systems including hygromorphic fibers that are useful in the chemical arts, such as in the manufacture of products, such as construction materials and, more particularly, to natural-based construction materials. In particular, the present disclosure relates to systems, materials, and methods for preparing certain natural-based construction materials including hygromorphic fibers configured cooperate with soil.

Dense ceramic-metal composites and components and methods of manufacturing. A method of manufacturing a ceramic-metal composite includes forming a metal containing component into preform having a desired shape and dimensions with pores therein, infiltrating the pores of the preform with a multi-element liquid reactant, and reacting the metal containing component with the multi-element liquid reactant in a displacement reaction at an elevated temperature to form a less porous ceramic-metal composite.

A system for decentralized machine learning may include a hub node in communication with a plurality of spoke nodes. The hub node may receive, from a spoke node, trained model parameters for a machine learning model. The hub node aggregate the received model parameters. The hub node may exchange the aggregated model parameters with another hub node via gossip-based communication creating updated model parameters. The hub node may transmit the updated model parameters to the spoke nodes. A spoke node may receive the updated model parameters from the hub node and model parameters from a second hub node. The spoke node may aggregate the updated model parameters with the model parameters from the second hub node. The spoke node may train the local model parameters using training data.