Results 161 to 170 of about 776,440 (313)

The Challenge of Handling Structured Missingness in Integrated Data Sources

Advanced Intelligent Discovery, EarlyView.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson, Marius Hofert, Eleni‐Rosalina Andrinopoulou, Niels Hagenbuch, Sarah McGough, Chris Harbron, Robin Mitra   +6 more
wiley   +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

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

The use of DNA barcodes to estimate phylogenetic diversity in forest communities of southern China. [PDF]

Ecol Evol, 2019
Liu J, Liu J, Shan YX, Ge XJ, Burgess KS.   +4 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Carbon storage in mountain cloud forest communities, Jalpan de Serra, Querétaro, México. [PDF]

Carbon Balance Manag
Elizabeth FR, Eugenia GCN, Sergey S, Citlalli LB, Velázquez-Rosas N.   +4 more
europepmc   +1 more source

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Mycorrhizal symbioses and tree diversity in global forest communities. [PDF]

Sci Adv
Jiang F, Pu X, Schmid B, Reich PB, Liang J, Abbasi AO, Aguirre-Gutiérrez J, Zambrano AMA, Altman J, Álvarez-González JG, Alves LF, Amani BHK, Ammer C, Aymard GA, Babu Kanda N, Bastian ML, Bastin JF, Bauters M, Boeckx P, Bondarchuk SN, Bondarev A, Brearley FQ, Brennan S, Briseño-Reyes J, Broadbent EN, Češljar G, Chen HYH, Chisholm C, Choi W, Cienciala E, Clark CJ, Collalti A, Corral-Rivas JJ, Dar JA, Dayanandan S, de-Miguel S, Dar AA, Derroire G, Djordjevic I, Van Do T, Doležal J, Dourdain A, Eyre T, Fandohan AB, Frizzera L, Gatti RC, Gianelle D, González Elizondo MS, Grieco E, Harris DJ, Hector A, Hérault B, Hui C, Imai N, Jagodziński AM, Ji C, Jiang L, Joly CA, Karminov VN, Kartawinata K, Kassi JN, Kearsley E, Keppel G, Khan ML, Klauberg C, Korznikov KA, Kothandaraman S, Kraxner F, Krivobokov L, Kucher D, Kumar A, Kvashnina A, Laurin GV, Leite RV, Libalah MB, Lonkina ES, Lu H, Luo S, Luo Y, Mackintosh E, Marshall AR, Martínez RV, Matula R, McDonald W, Narayanan A, Nava-Miranda MG, Naveenkumar J, Mendoza AM, Miścicki S, Moskalyuk T, Mukhortova L, Mukul SA, Nabuurs GJ, Neldner VJ, Nevenic R, N'Guessan AE, Ngugi M, Paquette A, Parfenova EI, Parren M, Parthasarathy N, Peri PL, Pfautsch S, Piedade MTF, Polyakova G, Poulsen AD, Poulsen JR, Pretzsch H, Rodeghiero M, Rutishauser E, Saikia P, Saner P, Schepaschenko D, Schöngart J, Searle EB, Sheil D, Shen Z, Shooner S, Shvidenko A, Silva CA, Sist P, Slik F, Song W, Souza AF, Stereńczak K, Sundarapandian S, Svátek M, Svoboda M, Tang Z, Targhetta N, Tchebakova N, Tikhonova E, Trethowan L, Vega-Nieva DJ, Verbeeck H, Vieira SA, Volle C, Vozmishcheva AS, Vozmitel FK, Wang HF, Wang S, Wang X, Wittmann F, Zheng C, Zhu B, Zo-Bi IC, Fang J, Wang Z.   +147 more
europepmc   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source