Results 51 to 60 of about 2,320,712 (255)
Inventory Optimization Model of Biomass Power Plant Considering Multiple Uncertainties
Zhongguo dianliThe formulation of inventory optimization strategies for biomass power plants is the basis for ensuring regional power supply. However, the seasonality and demand uncertainty of biofuels have brought great challenges to inventory optimization.
Jinliang ZHANG, Zeping HU
doaj +1 more source
Normalized convergence in stochastic optimization [PDF]
Annals of Operations Research, 1991 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuri M. Ermoliev, Vladimir I. Norkin
openaire +6 more sources
Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry
FEBS Letters, EarlyView.EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri +5 more
wiley +1 more source
Computational complexity of stochastic programming problems [PDF]
, 2005 Stochastic programming is the subfield of mathematical programming that considers optimization in the presence of uncertainty. During the last four decades a vast quantity of literature on the subject has appeared.
Dyer, M. +7 more
core +2 more sources
FROST—Fast row-stochastic optimization with uncoordinated step-sizes
EURASIP Journal on Advances in Signal Processing, 2019 In this paper, we discuss distributed optimization over directed graphs, where doubly stochastic weights cannot be constructed. Most of the existing algorithms overcome this issue by applying push-sum consensus, which utilizes column-stochastic weights ...
Ran Xin, Chenguang Xi, Usman A. Khan
doaj +1 more source
Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios
Mathematics, 2020 Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy ...
Diego Oliva +5 more
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
CoRR, 2019 49 pages, 2 tables, 9 ...
Quoc Tran-Dinh +3 more
openaire +3 more sources
Observer‐Based Adaptive Event‐Triggered Tracking Control for Fuzzy TS Systems With Premise Mismatch
International Journal of Adaptive Control and Signal Processing, EarlyView.This paper presents an adaptive logistic event‐triggered observer‐based tracking controller for Takagi‐Sugeno fuzzy systems under constrained inputs and network delays. Leveraging a hybrid LMI and Secretary Bird Optimization approach, this strategy significantly minimizes communication overhead and computational burden while ensuring optimal reference ...
Oussama Djadane +3 more
wiley +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source