Results 191 to 200 of about 776,491 (337)

Enhancing Q‐Learning via State‐Space Design for Active Battery Balancing

open access: yesBattery Energy, Volume 5, Issue 2, March 2026.
This study investigates the profound impact of state definition on the efficacy of Q‐learning for active battery balancing in lithium‐ion battery packs. We define and evaluate three distinct state representations—[State 1, 11‐state space definition], [State 2, 27‐state space definition], and [State 3, 81‐state space definition]—within a reinforcement ...
Fatemeh Ebrahimabadi   +2 more
wiley   +1 more source

Estimates of Dirichlet heat kernel for symmetric Markov processes [PDF]

open access: yesStochastic Processes and their Applications, 2015
T. Grzywny, Kyung-Youn Kim, P. Kim
semanticscholar   +1 more source

Bayesian Inference for Spatially‐Temporally Misaligned Data Using Predictive Stacking

open access: yesEnvironmetrics, Volume 37, Issue 2, March 2026.
ABSTRACT Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly
Soumyakanti Pan, Sudipto Banerjee
wiley   +1 more source

Forecasting Carbon Prices: A Literature Review

open access: yesJournal of Forecasting, Volume 45, Issue 2, Page 496-529, March 2026.
ABSTRACT Carbon emissions trading is utilized by a growing number of states as a significant tool for addressing greenhouse gas emissions (GHG), global warming problem and the climate crisis. Accurate forecasting of carbon prices is essential for effective policy design and investment strategies in climate change mitigation.
Konstantinos Bisiotis   +2 more
wiley   +1 more source

A comprehensive review of cluster methods for drug–drug interaction network

open access: yesQuantitative Biology, Volume 14, Issue 1, March 2026.
Abstract The detection of drug–drug interaction (DDI) is crucial to the rational use of drug combinations. Experimentally, DDI detection is time‐consuming and laborious. Currently, researchers have developed a variety of computational methods to predict DDI.
Shuyuan Cao   +3 more
wiley   +1 more source

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