Results 161 to 170 of about 353,149 (264)

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Efficient Signed Certificate Verification for IoT and V2V Messages via Blockchain Integration. [PDF]

Sensors (Basel)
Khoury D, Eledlebi K, Hamze K, Sayah J, Sondi P, Danach K, Semaan D, Farran H, Haddad S.   +8 more
europepmc   +1 more source

Human Factors Engineering Guidelines for Overhead Cranes

, 2013
Faith Chandler, H. Delgado
openalex   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Efficiency of a smart parking system in privacy-preserving using multi transaction mode consortium blockchain. [PDF]

Sci Rep
Durairaj A, Baskaran S, Baskaran A, Janakiraman R, Balachandran PK, Gundla R, Kannan S.   +6 more
europepmc   +1 more source

Reliability Engineering Methods for Distribution Systems - Influence of Differences between Cable Systems and Overhead Lines

, 2007
Anna Guldbrand
openalex   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

LSTM guided homomorphic encryption for threat-resistant IoT networks. [PDF]

Discov Comput
Kumar S, Deora SS, Kumar T, Sharma P, Cheng X, Garg V.   +5 more
europepmc   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Improving efficiency in smart grid monitoring using hybrid classification and dimensionality reduction. [PDF]

Sci Rep
Kumar TS, Kesavaraja D.
europepmc   +1 more source