Results 31 to 40 of about 555,552 (274)

Lyapunov-Guided Energy Scheduling and Computation Offloading for Solar-Powered WSN

Applied Sciences, 2023
To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge computing (MEC) technologies to develop a solar-powered ...
Juan Gao, Runze Wu, Jianhong Hao
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, Pedro Copado, Salvador Hinojosa, Javier Panadero, Daniel Riera, Angel A. Juan   +5 more
doaj   +1 more source

Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization [PDF]

, 2013
In this paper, we present a new stochastic algorithm, namely the stochastic block mirror descent (SBMD) method for solving large-scale nonsmooth and stochastic optimization problems. The basic idea of this algorithm is to incorporate the block-coordinate
Dang, Cong D., Lan, Guanghui
core  

Stochastic MPC Design for a Two-Component Granulation Process

, 2017
We address the issue of control of a stochastic two-component granulation process in pharmaceutical applications through using Stochastic Model Predictive Control (SMPC) and model reduction to obtain the desired particle distribution.
alambeigi, hashemian, hashemian
core   +1 more source

Time after time – circadian clocks through the lens of oscillator theory

FEBS Letters, EarlyView.
Oscillator theory bridges physics and circadian biology. Damped oscillators require external drivers, while limit cycles emerge from delayed feedback and nonlinearities. Coupling enables tissue‐level coherence, and entrainment aligns internal clocks with environmental cues.
Marta del Olmo, Carolin Ector, Hanspeter Herzel   +2 more
wiley   +1 more source

Inventory Optimization Model of Biomass Power Plant Considering Multiple Uncertainties

Zhongguo dianli
The 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

Optimal Stochastic Enhancement of Photoionization [PDF]

Physical Review Letters, 2007
The effect of noise on the nonlinear photoionization of an atom due to a femtosecond pulse is investigated in the framework of the stochastic Schr dinger equation. A modest amount of white noise results in an enhancement of the net ionization yield by several orders of magnitude, giving rise to a form of quantum stochastic resonance.
Singh, K., Rost, J.
openaire   +4 more sources

LDAcoop: Integrating non‐linear population dynamics into the analysis of clonogenic growth in vitro

Molecular Oncology, EarlyView.
Limiting dilution assays (LDAs) quantify clonogenic growth by seeding serial dilutions of cells and scoring wells for colony formation. The fraction of negative wells is plotted against cells seeded and analyzed using the non‐linear modeling of LDAcoop.
Nikko Brix, Daniel Samaga, Katharina Gehr, Benedek Dankó, Mohamed Schumann, Guido Drexler, Ahmed Alnatsha, Georg Beyer, Ujjwal Mahajan, Martin Selmansberger, Julia Mayerle, Claus Belka, Horst Zitzelsberger, Kirsten Lauber   +13 more
wiley   +1 more source

Signal Recovery by Stochastic Optimization [PDF]

Automation and Remote Control, 2019
We discuss an approach to signal recovery in Generalized Linear Models (GLM) in which the signal estimation problem is reduced to the problem of solving a stochastic monotone variational inequality (VI). The solution to the stochastic VI can be found in a computationally efficient way, and in the case when the VI is strongly monotone we derive finite ...
Juditsky, Anatoli, B., Nemirovski, Arkadii, S.   +1 more
openaire   +3 more sources

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source