Results 61 to 70 of about 6,180 (210)

Distributionally robust possibilistic optimization problems

Fuzzy Sets and Systems, 2023
In this paper a class of optimization problems with uncertain linear constraints is discussed. It is assumed that the constraint coefficients are random vectors whose probability distributions are only partially known. Possibility theory is used to model the imprecise probabilities.
Guillaume, Romain, Kasperski, Adam, Zieliński, Paweł   +2 more
openaire   +3 more sources

Integrated genomic and proteomic profiling reveals insights into chemoradiation resistance in cervical cancer

Molecular Oncology, EarlyView.
A comprehensive genomic and proteomic analysis of cervical cancer revealed STK11 and STX3 as a potential biomarkers of chemoradiation resistance. Our study demonstrated EGFR as a therapeutic target, paving the way for precision strategies to overcome treatment failure and the DNA repair pathway as a critical mechanism of resistance.
Janani Sambath, Irene A. George, Srikanth S. Manda, Prasanth Ariyannur, Ekta R. Dhawale, Raja Sekhar Kommu, Rajan Datar, Darshana Patil, Vinita Trivedi, Manisha Singh, Kumar Prabhash, Sewanti Limaye, Richa Chauhan, Prashant Kumar   +13 more
wiley   +1 more source

Distributionally robust optimization for fire station location under uncertainties

Scientific Reports, 2022
Emergency fire service (EFS) systems provide rescue operations for emergencies and accidents. If properly designed, they can decrease property loss and mortality.
Jinke Ming, Jean-Philippe P. Richard, Rongshui Qin, Jiping Zhu   +3 more
doaj   +1 more source

Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Digital Communications and Networks, 2023
Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emissions and electricity expenses of base stations.
Pengfei Du, Hongjiang Lei, Imran Shafique Ansari, Jianbo Du, Xiaoli Chu   +4 more
doaj   +1 more source

Convex Relaxations and Approximations of Chance-Constrained AC-OPF Problems

, 2018
This paper deals with the impact of linear approximations for the unknown nonconvex confidence region of chance-constrained AC optimal power flow problems. Such approximations are required for the formulation of tractable chance constraints.
Chatzivasileiadis, Spyros, Halilbasic, Lejla, Pinson, Pierre   +2 more
core   +1 more source

Calibration of Distributionally Robust Empirical Optimization Models [PDF]

Operations Research, 2017
In “Calibration of Robust Empirical Optimization Models,” Gotoh, Kim, and Lim study the statistical properties of ɸ-divergence distributionally robust optimization with concave rewards. They show that worst-case sensitivity of the expected reward to deviations from the nominal is equal to the in-sample variance and that significant out-of-sample ...
Jun‐ya Gotoh, Michael Jong Kim, Andrew E. B. Lim   +2 more
openaire   +4 more sources

In vitro properties of patient serum predict clinical outcome after high dose rate brachytherapy of hepatocellular carcinoma

Molecular Oncology, EarlyView.
Following high dose rate brachytherapy (HDR‐BT) for hepatocellular carcinoma (HCC), patients were classified as responders and nonresponders. Post‐therapy serum induced increased BrdU incorporation and Cyclin E expression of Huh7 and HepG2 cells in nonresponders, but decreased levels in responders.
Lukas Salvermoser, Jan Niklas Schäfer, Shraga Nahum Goldberg, Philipp Maximilian Kazmierczak, Moritz Nikolaus Gröper, Philipp Franz Linden, Elif Öcal, Tanja Burkard, Stefanie Corradini, Najib Ben Khaled, Moritz Wildgruber, Max Seidensticker, Jens Ricke, Matthias Stechele, Marianna Alunni‐Fabbroni   +14 more
wiley   +1 more source

Distributionally robust optimization of capacity configuration in P2G stations considering the impact of wide power fluctuations

Zhejiang dianli
The volatility of renewable energy generation accelerates the aging of water distributionally robust optimization within Power-to-Gas （P2G） stations. Existing optimization models fail to effectively account for the impact of wide power fluctuations on ...
SUN Yifan, WANG Lin, JIN Xiao
doaj   +1 more source

Sinkhorn Distributionally Robust Optimization

Operations Research
Entropy-Regularized Wasserstein Distributionally Robust Optimization Uncertainty in data poses a central challenge in operations research. Distributionally robust optimization (DRO) offers a principled framework for addressing this challenge by producing solutions resilient to distributional variations.
Jie Wang, Rui Gao, Yao Xie
openaire   +2 more sources

Data-Driven Distributionally Robust Optimization over Time

INFORMS Journal on Optimization, 2023
Stochastic optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters. Because the latter is often unknown, distributionally robust optimization (DRO) provides a strong alternative that determines the best guaranteed solution over a set of ...
Kevin-Martin Aigner, Andreas Bärmann, Kristin Braun, Frauke Liers, Sebastian Pokutta, Oskar Schneider, Kartikey Sharma, Sebastian Tschuppik   +7 more
openaire   +2 more sources