Results 51 to 60 of about 1,503,716 (266)
, 2016 Composite convex optimization models arise in several applications, and are especially prevalent in inverse problems with a sparsity inducing norm and in general convex optimization with simple constraints.
Hovhannisyan, Vahan +2 more
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 +2 more
wiley +1 more source
Mathematical Biosciences and Engineering, 2022 Supply chain network is important for the enterprise to improve the operation and management, but has become more complicated to optimize in reality. With the consideration of multiple objectives and constraints, this paper proposes a constrained large ...
Xin Zhang +4 more
doaj +1 more source
A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization
, 2014 We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES.
Hansen N. +3 more
core +1 more source
FEBS Letters, EarlyView.Cryptochrome and PAS/LOV proteins play intricate roles in circadian clocks where they act as both sensors and mediators of protein–protein interactions. Their ubiquitous presence in signaling networks has positioned them as targets for small‐molecule therapeutics. This review provides a structural introduction to these protein families.
Eric D. Brinckman +2 more
wiley +1 more source
In situ molecular organization and heterogeneity of the Legionella Dot/Icm T4SS
FEBS Letters, EarlyView.We present a nearly complete in situ model of the Legionella Dot/Icm type IV secretion system, revealing its central secretion channel and identifying new components. Using cryo‐electron tomography with AI‐based modeling, our work highlights the structure, variability, and mechanism of this complex nanomachine, advancing understanding of bacterial ...
Przemysław Dutka +11 more
wiley +1 more source
IEEE Access, 2017 With the advent of big data era, complex optimization problems with many objectives and large numbers of decision variables are constantly emerging. Traditional research about multi-objective particle swarm optimization (PSO) focuses on multi-objective ...
Bin Cao +6 more
doaj +1 more source
Elite Directed Particle Swarm Optimization with Historical Information for High-Dimensional Problems
Mathematics, 2022 High-dimensional optimization problems are ubiquitous in every field nowadays, which seriously challenge the optimization ability of existing optimizers.
Qiang Yang +4 more
doaj +1 more source
Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems [PDF]
, 2014 In this paper we review basic and emerging models and associated algorithms for large-scale tensor networks, especially Tensor Train (TT) decompositions using novel mathematical and graphical representations. We discus the concept of tensorization (i.e.,
Cichocki, Andrzej
core
FEBS Letters, EarlyView.We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee +3 more
wiley +1 more source