Results 181 to 190 of about 99,963 (219)

Knowledge Spillover, Trust, Effort, and Error Exposure in Peer‐Assisted Learning

open access: yesTopics in Cognitive Science, EarlyView.
Abstract Peer‐assisted learning has the potential to improve learning in academic settings and beyond. However, the cognitive and motivational mechanisms of learning through interaction with other learners are not fully understood. Here, we present an empirical study in which we compare a peer‐assisted learning condition with two individual learning ...
Ion Juvina   +5 more
wiley   +1 more source

Superlative Objoid Constructions in British and American English

open access: yesWorld Englishes, EarlyView.
ABSTRACT This paper investigates regional variation in Superlative Objoid constructions (SOCs) and their prepositional variant (at‐SOCs). SOCs combine a possessive pronoun with a superlative adjective. These function as manner‐degree modifiers in a context where the possessive is in postverbal position and correlative with the subject, as in they tried
Tamara Bouso, Marianne Hundt
wiley   +1 more source

A digital twin framework for urban parking management and mobility forecasting. [PDF]

open access: yesNat Commun
Piccialli F   +5 more
europepmc   +1 more source

A novel dual‐decomposition method for non‐convex two‐stage stochastic mixed‐integer quadratically constrained quadratic problems

open access: yesInternational Transactions in Operational Research, Volume 33, Issue 5, Page 3128-3157, September 2026.
Abstract We propose the novel p‐branch‐and‐bound method for solving two‐stage stochastic programming problems whose deterministic equivalents are represented by non‐convex mixed‐integer quadratically constrained quadratic programming (MIQCQP) models. The precision of the solution generated by the p‐branch‐and‐bound method can be arbitrarily adjusted by
Nikita Belyak, Fabricio Oliveira
wiley   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

open access: yesApplied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang   +3 more
wiley   +1 more source

Home - About - Disclaimer - Privacy