Results 71 to 80 of about 3,566,037 (267)

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

Boosted regression statistics.

, 2016
Boosted regression statistics.
Denny Meyer (828573), Julie A. Pasco (223870), Michael Berk (343706), Lana J. Williams (657175), Felice N. Jacka (343702), Joanna F. Dipnall (2157217), Seetal Dodd (828571)   +6 more
core   +1 more source

Artificial Intelligence‐Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae

Advanced Science, EarlyView.
Integrated artificial intelligence (AI) ensembles identify highly potent repurposed drugs against Streptococcus pneumoniae. Prospective virtual screening of 6747 drugs uncovers nine active candidates, including thiostrepton. This drug exhibits low picomolar potency and bypasses established resistance in multidrug‐resistant strains, demonstrating an ...
Joshua S. Fitch, Muhammad D. Ariadi, Min Jung Kwun, Leonie Howells, Saiveth Hernandez‐Hernandez, Nicholas J. Croucher, Pedro J. Ballester   +6 more
wiley   +1 more source

Boosted beta regression covariates.

, 2019
Boosted beta regression covariates.
J. Perrine (6685532), D. M. Parker (4951762), F. G. Lemos (6685580), R. Sandfort (6685463), E. Meisingset (6685487), P. M. Kappeler (5481869), J. Melzheimer (6685376), A. Caso (6685529), M. Zaccaroni (6685439), N. Selva (6685625), T. Zwijacz-Kozica (6685541), R. Steinmetz (6685658), B. V. Weckworth (6685586), P. Sunde (6685634), T. L. Scheppers (6685571), A. Berger (4901668), W. Ullmann (6685394), P. Šustr (6685595), C. Rouco (6685391), C. Fischer (6685451), B. Buuveibaatar (6685415), C. Richard-Hansen (6685613), J. D. C. Linnell (6685436), B. Cristescu (6685553), P. Kaczensky (6685412), J. E. F. Oshima (6685445), C. Tellaeche (6685535), W. T. Bean (6685550), U. Voigt (6685382), Ö. Johansson (6685427), A. Ekori (6685607), M. Klapproth (6685472), M. Drouilly (6685469), L. H. Swanepoel (6685583), N. Morellet (6685379), V. Menges (6685568), N. Balkenhol (5741027), D. Bockmuhl (6685601), T. Grant (6685655), N. H. Jørgensen (6685400), B. L. Allen (6685385), A. Songhurst (6685448), M. Heurich (6685370), Ø. Holand (6685397), S. Said (6685502), F. C. Azevedo (6685577), S. Périquet (6685556), S. LaPoint (6685430), L. Pyritz (6685649), F. Tortato (6685526), A. Devlin (6685508), A. Mysterud (6685373), P. Marchand (6685361), B. Gehr (6685388), M. P. G. Hofman (6685352), A. T. Vanak (6685493), M. S. Farhadinia (6685631), D. Armenteras (6685667), F. Cagnacci (6685406), I. Palmegiani (6685421), S. Karpanty (6685562), S. Kachel (6685679), S. C. Schai-Braun (6685574), C. Bonenfant (6685466), M. Thaker (6685490), R. Portas (6685637), J. J. Thompson (6685457), S. Streif (6685460), E. Robardet (6685622), M. W. Hayward (6685355), J. Casaer (6685604), M. R. Boudreau (6685505), S. Nicoloso (6685496), G. K. Mann (6685646), G. Steinheim (6685403), D. van de Vyver (6685616), R. Reyna-Hurtado (6685619), L. Swedell (6685661), P. Kjellander (5037974), K. Jerina (6685424), R. J. Welch (6685664), C. L. Appel (6685544), R. Graeber (6685499), J. C. Nifong (6685517), O. R. Bidder (6685670), H. B. Quigley (6685523), G. McCulloch (6685592), J. C. Payne (6685418), A. Stronza (6685481), L. Wilmott (6685484), C. M. Rolandsen (2930700), A. Rosenblatt (6685676), J. Odden (6685520), J. A. May-Junior (6685514), K. J. Teichman (6685559), D. Zinner (6685475), F. Zieba (6685538), R. Yarnell (6685478), K. Black (4421269), R. Bayrakcismith (6685433), A. Bertassoni (6685589), M. Heim (6685358), F. Urbano (6685367), M. L. S. Jorge (6685442), M. Kroeschel (6685409), A. Licoppe (6685565), T. M. González (6685673), V. Athreya (6685598), J. Mattisson (6685364), R. Hoogesteijn (6685511), H. Ambarli (6685640), I. Axsom (6685547), R. Berzins (6685643), R. T. Mc Bride Jr. (6685454), A. Sergiel (6685628), D. Melovski (6685610), A. Payne (3362888), C. Bissett (6685652)   +117 more
core   +1 more source

Utilization of a boosted regression tree framework for prediction of dissolved phosphorus concentrations throughout the High Plains aquifer region [PDF]

, 2022
Groundwater-derived phosphorus has often been dismissed as a significant contributor towards surface water eutrophication, however, this dismissal is unwarranted, making the quantification of phosphorus concentrations in groundwater systems immensely ...
Temple, Jeffrey M
core  

Machine Learning‐Driven Design of Multicomponent Bone Inorganic Matrix Mimicking Scaffolds for Osteogenesis Enhanced by Neurogenesis

Advanced Science, EarlyView.
Schematic illustration of development of experimental datasets and algorithm models, screening and preparation of the scaffolds and their applications in vivo. ABSTRACT Bone defects require materials with osteogenic, neurogenic, and angiogenic activity, yet designing such materials within high‐dimensional compositional spaces remains challenging. Here,
Kunlu Lin, Ying Yang, Chuyao Zhou, Hanyue Mao, Zhiquan Zhang, Libangxi Liu, Xiaoying Yu, Haoming Liu, Long Liu, Xiaoyan Wang   +9 more
wiley   +1 more source

A General Framework for Fair Regression

Entropy, 2019
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian ...
Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne, Stephen Roberts   +3 more
doaj   +1 more source

Appendix C. Boosted tree examples in R.

, 2016
Boosted tree examples in ...
Glenn De'ath (376371)
core   +1 more source

BOOSTED REGRESSION TREES [PDF]

, 2020
Pohon regresi merupakan teknik analisis data yang bertujuan untuk mengetahui pengaruh semua peubah penjelas terhadap peubah responnya. Namun, ternyata metode pohon regresi memiliki kelemahan yaitu struktur hierarkisnya memungkinkan terjadinya varians ...
Wulan Dian Pramiesti, -
core  

Drought Stress Mediated Changes in Food Crops: Mechanisms and Remediation Strategies

Advanced Science, EarlyView.
This work presents a multi‐scale framework for flavonoid‐mediated drought tolerance in staple food crops, integrating ROS redox regulation, antioxidant defense, rhizosphere microbial interactions, and sustainable agronomic practices. This interdisciplinary model provides core insights to develop climate‐resilient cropping systems and enhance global ...
Xiaoyi Duan, Litao Wang, Tuuli‐Marjaana Koski, Lizhe An, Thomas Efferth, Yujie Fu   +5 more
wiley   +1 more source