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G-DaM: A Distributed Data Storage with Blockchain Framework for Management of Groundwater Quality Data (open access)

G-DaM: A Distributed Data Storage with Blockchain Framework for Management of Groundwater Quality Data

Authors discuss their use of a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts to forecast groundwater availability. Their work demonstrates a modern and innovative approach combining Distributed Data Storage and Blockchain technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations.
Date: November 11, 2022
Creator: Vangipuram, Sukrutha L. T.; Mohanty, Saraju P.; Kougianos, Elias & Ray, Chittaranjan
Object Type: Article
System: The UNT Digital Library
Texas Register, Volume 47, Number 45, Pages 7491-7606, November 11, 2022 (open access)

Texas Register, Volume 47, Number 45, Pages 7491-7606, November 11, 2022

A weekly publication, the Texas Register serves as the journal of state agency rulemaking for Texas. Information published in the Texas Register includes proposed, adopted, withdrawn and emergency rule actions, notices of state agency review of agency rules, governor's appointments, attorney general opinions, and miscellaneous documents such as requests for proposals. After adoption, these rulemaking actions are codified into the Texas Administrative Code.
Date: November 11, 2022
Creator: Texas. Secretary of State.
Object Type: Journal/Magazine/Newsletter
System: The Portal to Texas History
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations (open access)

Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations

Article provides a general reweighting framework based on anisotropic diffusion maps for manifold learning that takes into account that the learning data set is sampled from a biased probability distribution. The authors show that their proposed framework can be used in many manifold learning techniques on data from both standard and enhanced sampling simulations.
Date: November 11, 2022
Creator: Rydzewski, Jakub; Chen, Ming; Ghosh, Tushar K. & Valsson, Omar
Object Type: Article
System: The UNT Digital Library