Results 101 to 110 of about 2,032 (302)
Effective Scenarios in Distributionally Robust Optimization
, 2018 Traditional stochastic optimization assumes that the probability distribution of uncertainty is known. However, in practice, the probability distribution oftentimes is not known or cannot be accurately approximated. One way to address such distributional
Bayraksan, Guzin
core
Single‐molecule DNA flow‐stretch assays for high‐throughput DNA–protein interaction studies
FEBS Open Bio, EarlyView.We describe an optimised single‐molecule DNA flow‐stretch assay that visualises DNA–protein interactions in real time. Linear DNA fragments are tethered to a surface and stretched by buffer flow for fluorescence imaging. Using λ and φX174 DNA, this protocol enhances reproducibility and accessibility, providing a versatile approach for studying diverse ...
Ayush Kumar Ganguli +8 more
wiley +1 more source
From Data to Decisions: Distributionally Robust Optimization is Optimal
, 2018 Data-driven stochastic programming aims to find a procedure that transforms time series data to a near-optimal decision (a prescriptor) and to a prediction of this decision's expected cost under the unknown data-generating distribution (a predictor).
Kuhn, Daniel
core
Distributionally Robust Energy Optimization with Renewable Resource Uncertainty
MathematicsWith the increasing prevalence of intermittent power generation, the volatility, intermittency, and randomness of renewable energy pose significant challenges to the planning and operation of distribution networks.
Zhangyi Wang +5 more
doaj +1 more source
FEBS Open Bio, EarlyView.This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Distributionally Robust Shape and Topology Optimization
This article aims to introduce the paradigm of distributional robustness from the field of convex optimization to tackle optimal design problems under uncertainty. We consider realistic situations where the physical model, and thereby the cost function of the design to be minimized depend on uncertain parameters.
Charles Dapogny +2 more
openaire +2 more sources
Distributionally Robust Newsvendor Problems with Variation Distance
, 2018 We use distributionally robust stochastic programs (DRSP) to model a general class of newsvendor problems where the underlying demand distribution is unknown, and so the goal is to find an order quantity that minimizes the worst-case expected cost among ...
Homem-de-Mello, Tito
core
A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks. [PDF]
Transp Res Part C Emerg Technol, 2023 Xu Y, Wandelt S, Sun X.
europepmc +1 more source
Systemic dysregulation of apolipoproteins in amyotrophic lateral sclerosis serum
FEBS Open Bio, EarlyView.Amyotrophic lateral sclerosis (ALS) is a fatal disease that damages motor neurons. This study found that people with ALS show significant changes in blood fats and the proteins that carry them. Several apolipoproteins were higher, lipid balances were altered, and normal protein–lipid relationships were disrupted.
Finula I. Isik +6 more
wiley +1 more source
A new distributionally robust reward-risk model for portfolio optimization
Open MathematicsA new distributionally robust ratio optimization model is proposed under the known first and second moments of the uncertain distributions. In this article, both standard deviation (SD) and conditional value-at-risk (CVaR) are used to measure the risk ...
Zhou Yijia, Xu Lijun
doaj +1 more source