Results 171 to 180 of about 1,003,903 (269)
Olympiad-level formal mathematical reasoning with reinforcement learning. [PDF]
Hubert T +38 more
europepmc +1 more source
A Survey on Proof of Sequential Work: Development, Security Analysis, and Application Prospects. [PDF]
Zhang J +7 more
europepmc +1 more source
Counting Rankings of Tree-Child Networks. [PDF]
Zhang Q, Steel M.
europepmc +1 more source
ABSTRACT The growing impacts of climate change and energy scarcity demand environmentally sustainable refrigeration solutions. This study investigates eco‐friendly refrigerants (R152a, R290 and R600a) and their optimized ternary mixture as alternatives to R134a. Four blend compositions were analysed using experimental testing and REFPROP 10.0 modelling
M. Periyasamy, P. Senthilkumar
wiley +1 more source
Singularity in nonlinear systems: differential inclusion model for the standard and transformed fractional pantograph equation. [PDF]
Mobayen S +4 more
europepmc +1 more source
ABSTRACT In this study, the actual route of methylene blue (MB) dye adsorption by using fabricated polyfunctional activated carbon–copper oxide nanowires (AC@CuO‐NWs) from bulky wastewater bodies has been investigated. To better understand the exact pathway of the adsorption process, a prominent statistical physics formalism or grand canonical ...
Abdellatif Sakly +7 more
wiley +1 more source
Numerical robustness of COVID-19 model with Lyapunov stability analysis. [PDF]
Pandey A, Ghosh S.
europepmc +1 more source
Abstract This paper tackles the problem of robust and accurate fixed‐time tracking in human–robot interaction and deals with uncertainties. This work introduces a control approach for a wearable exoskeleton designed specifically for rehabilitation tasks.
Mahmoud Abdallah +4 more
wiley +1 more source
Prioritized Aczel-Alsina aggregation operators under p, q-quasirung orthopair fuzzy environment for sustainable supplier selection in new energy vehicle industry. [PDF]
Ali J, Al-Kenani AN, Syam MI.
europepmc +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source

