Modelling and Simulation in Life and Materials Sciences: Envíos recientes
Now showing items 2140 of 147

Spectral properties of hyperbolic nanonetworks with tunable aggregation of simplexes
(2019)Cooperative selfassembly is a ubiquitous phenomenon found in natural systems which is used for designing nanostructured materials with new functional features. Its origin and mechanisms, leading to improved functionality ... 
Coupled thermoelectromechanical models for thermal ablation of biological tissues and heat relaxation time effects
(2019)Thermal ablation is a widely applied electrosurgical process in medical treatment of soft biological tissues. Numerical modeling and simulations play an important role in prediction of temperature distribution and ... 
Computational Analysis of Pulsed Radiofrequency Ablation in Treating Chronic Pain
(201906)In this paper, a parametric study has been conducted to evaluate the effects of frequency and duration of the short burst pulses during pulsed radiofrequency ablation (RFA) in treating chronic pain. Affecting the brain and ... 
Radiofrequency Ablation for Treating Chronic Pain of Bones: Effects of Nerve Locations
(201905)The present study aims at evaluating the effects of target nerve location from the bone tissue during continuous radiofrequency ablation (RFA) for chronic pain relief. A generalized threedimensional heterogeneous computational ... 
Functional Geometry of Human Connectomes
(201908)Mapping the brain imaging data to networks, where nodes represent anatomical brain regions and edges indicate the occurrence of fiber tracts between them, has enabled an objective graphtheoretic analysis of human connectomes. ... 
An optimal scaling to computationally tractable dimensionless models: Study of latex particles morphology formation
(202002)In modelling of chemical, physical or biological systems it may occur that the coefficients, multiplying various terms in the equation of interest, differ greatly in magnitude, if a particular system of units is used. Such ... 
Modified Hamiltonian Monte Carlo for Bayesian Inference
(20190722)The Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in computational statistics. We show that performance of HMC can be significantly improved by incorporating importance sampling and ... 
Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation
(201907)Our inability to predict the behavior of biological systems severely hampers progress in bioengineering and biomedical applications. We cannot predict the effect of genotype changes on phenotype, nor extrapolate the ... 
A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630
(201906)Rhodococcus opacus PD630 metabolizes aromatic substrates and naturally produces branchedchain lipids, which are advantageous traits for lignin valorization. To provide insights into its lignocellulose hydrolysate utilization, ... 
Exploring Liion conductivity in cubic, tetragonal and mixedphase Alsubstituted Li7La3Zr2O12 using atomistic simulations and effective medium theory
(20190815)Garnet Li7La3Zr2O12 (LLZO) is a promising solid electrolyte candidate for solidstate Liion batteries, but at room temperature it crystallizes in a poorly Liion conductive tetragonal phase. To this end, partial substitution ... 
Machine learning framework for assessment of microbial factory performance
(20190101)Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess ... 
Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning
(20190101)The Design–Build–Test–Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient ... 
Atomistic Insight into Ion Transport and Conductivity in Ga/AlSubstituted Li$_7$La$_3$Zr$_2$O$_{12}$ Solid Electrolytes
(20180109)Garnetstructured Li$_{7}$La$_{3}$Zr$_{2}$O$_{12}$ is a promising solid electrolyte for nextgeneration solidstate Li batteries. However, sufficiently fast Liion mobility required for battery applications only emerges ... 
No Time at the End of the Tunnel
(20180821)Modern attosecond experiments seek to provide an insight into a long standing question: “how much time does a tunnelling particle spend in the barrier?” Traditionally, quantum theory relates this duration to the delay ... 
Multistage splitting integrators for sampling with modified Hamiltonian Monte Carlo methods
(20180717)Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two popular sampling approaches: Hamiltonian Monte Carlo (HMC) and importance sampling. As in the HMC case, the bulk of the computational cost of ... 
Synthesis of Monodisperse Spherical Nanocrystals
(20160129)Nanoparticles, small units of matter with dimensions in the range 1100 nm, exhibit many advantageous sizedependent magnetic, electrical, chemical and optical prop erties, which are not observed at the microscale or ... 
On the Analysis of TrajectoryBased Search Algorithms: When is it Beneficial to Reject Improvements?
(2018)We investigate popular trajectorybased algorithms inspired by biology and physics to answer a question of general significance: when is it beneficial to reject improvements? A distinguishing factor of SSWM (Strong Selection ... 
Probabilistic Modelling of Classical and Quantum Systems
(20180614)While probabilistic modelling has been widely used in the last decades, the quantitative prediction in stochastic modelling of real physical problems remains a great challenge and requires sophisticated mathematical models ... 
Adapting Hybrid Monte Carlo methods for solving complex problems in life and materials sciences
(20180511)Efficient sampling is the key to success of molecular simulation of complex physical systems. Still, a unique recipe for achieving this goal is unavailable. Hybrid Monte Carlo (HMC) is a promising sampling tool offering a ... 
A Machine Learning Approach to Predict Metabolic Pathway Dynamics from Time Series Multiomics Data
(201805)New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict ...