Results 111 to 120 of about 10,524 (182)

Scenario‐Based Platoon Lane Network Design

open access: yesNetworks, Volume 87, Issue 4, Page 443-459, June 2026.
ABSTRACT A truck platoon is a set of trucks that drive behind one another at short headways to save fuel, reduce emissions, and improve traffic throughput. Despite the potential benefits of platooning, road operators have raised concerns about the impact of platoons on surrounding traffic.
Anirudh Kishore Bhoopalam   +2 more
wiley   +1 more source

Heuristics and optimal solutions to the breadth-depth dilemma. [PDF]

open access: yesProc Natl Acad Sci U S A, 2020
Moreno-Bote R   +3 more
europepmc   +1 more source

Multi‐Goal‐Oriented Anisotropic Error Control and Mesh Adaptivity for Time‐Dependent Convection‐Dominated Problems

open access: yesProceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT In this work, we present an anisotropic multi‐goal error control based on the dual weighted residual (DWR) method for time‐dependent convection–diffusion–reaction (CDR) equations. Motivated by former work, we combine multiple goals to single error functionals with weights chosen as algorithmic parameters.
Markus Bause   +5 more
wiley   +1 more source

Spectral State Compression of Markov Processes. [PDF]

open access: yesIEEE Trans Inf Theory, 2020
Zhang A, Wang M.
europepmc   +1 more source

Distributed Optimization of Finite Condition Number for Laplacian Matrix in Multi‐Agent Systems

open access: yesInternational Journal of Robust and Nonlinear Control, Volume 36, Issue 9, Page 5030-5043, June 2026.
ABSTRACT This paper addresses the distributed optimization of the finite condition number of the Laplacian matrix in multi‐agent systems. The finite condition number, defined as the ratio of the largest to the second smallest eigenvalue of the Laplacian matrix, plays an important role in determining the convergence rate and performance of consensus ...
Yicheng Xu, Faryar Jabbari
wiley   +1 more source

Unsupervised Time‐Event Probabilistic Classification Using Large Panels of Time Series

open access: yesStatistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 3, June 2026.
ABSTRACT This study presents a framework to perform unsupervised time‐event probabilistic classification using time series data of large cross‐sectional dimension. These datasets often exhibit complexities such as non‐linearities, structural breaks, asynchronicity, missing data, and outliers; which hampers their analysis and modeling.
Máximo Camacho   +2 more
wiley   +1 more source

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