Results 191 to 200 of about 49,971 (286)

Biotechnology systems engineering: preparing the next generation of bioengineers. [PDF]

Front Syst Biol
Espinel-Ríos S.
europepmc   +1 more source

Mathematical competition in modeling

Mathematical Modelling, 1985
openaire   +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

DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning. [PDF]

Nature
Guo D, Yang D, Zhang H, Song J, Wang P, Zhu Q, Xu R, Zhang R, Ma S, Bi X, Zhang X, Yu X, Wu Y, Wu ZF, Gou Z, Shao Z, Li Z, Gao Z, Liu A, Xue B, Wang B, Wu B, Feng B, Lu C, Zhao C, Deng C, Ruan C, Dai D, Chen D, Ji D, Li E, Lin F, Dai F, Luo F, Hao G, Chen G, Li G, Zhang H, Xu H, Ding H, Gao H, Qu H, Li H, Guo J, Li J, Chen J, Yuan J, Tu J, Qiu J, Li J, Cai JL, Ni J, Liang J, Chen J, Dong K, Hu K, You K, Gao K, Guan K, Huang K, Yu K, Wang L, Zhang L, Zhao L, Wang L, Zhang L, Xu L, Xia L, Zhang M, Zhang M, Tang M, Zhou M, Li M, Wang M, Li M, Tian N, Huang P, Zhang P, Wang Q, Chen Q, Du Q, Ge R, Zhang R, Pan R, Wang R, Chen RJ, Jin RL, Chen R, Lu S, Zhou S, Chen S, Ye S, Wang S, Yu S, Zhou S, Pan S, Li SS, Zhou S, Wu S, Yun T, Pei T, Sun T, Wang T, Zeng W, Liu W, Liang W, Gao W, Yu W, Zhang W, Xiao WL, An W, Liu X, Wang X, Chen X, Nie X, Cheng X, Liu X, Xie X, Liu X, Yang X, Li X, Su X, Lin X, Li XQ, Jin X, Shen X, Chen X, Sun X, Wang X, Song X, Zhou X, Wang X, Shan X, Li YK, Wang YQ, Wei YX, Zhang Y, Xu Y, Li Y, Zhao Y, Sun Y, Wang Y, Yu Y, Zhang Y, Shi Y, Xiong Y, He Y, Piao Y, Wang Y, Tan Y, Ma Y, Liu Y, Guo Y, Ou Y, Wang Y, Gong Y, Zou Y, He Y, Xiong Y, Luo Y, You Y, Liu Y, Zhou Y, Zhu YX, Huang Y, Li Y, Zheng Y, Zhu Y, Ma Y, Tang Y, Zha Y, Yan Y, Ren ZZ, Ren Z, Sha Z, Fu Z, Xu Z, Xie Z, Zhang Z, Hao Z, Ma Z, Yan Z, Wu Z, Gu Z, Zhu Z, Liu Z, Li Z, Xie Z, Song Z, Pan Z, Huang Z, Xu Z, Zhang Z, Zhang Z.   +193 more
europepmc   +1 more source

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

CRISPR-Cas is beneficial in plasmid competition, but limited by competitor toxin-antitoxin activity when horizontally transferred. [PDF]

PLoS Biol
Sünderhauf D, Ringger JR, Payne LJ, Pinilla-Redondo R, Gaze WH, Brown SP, van Houte S.   +6 more
europepmc   +1 more source

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

Competitive oscillatory dynamics in excitable neuron networks. [PDF]

Front Netw Physiol
Zheng Z, Yan L, Li T, Liu J, Wang L, Qian Y.   +5 more
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

A Roadmap for the Future of Systems Biology in Cancer Research. [PDF]

Cancer Res
Wiley HS, Lopez CF, Rodin AS, Rockne RC, Yankeelov TE, Sauro HM, Hassan G, Prabhakaran S, Demir E, Hu M, Fuxman Bass J, Luddy KA, Newman H, Mochel JP, Costello JC, Xing J, Afify SM, Acar A, Haden Gephart M, Feng S, Banks O, Banerjee J, Clayton A, Hill DE, Sweis RF, Umeh-Garcia MC, Su Y, Meyer AS.   +27 more
europepmc   +1 more source