Automated Generation of Optimal Steel Sequences with Discrete Action, Status, and Interaction Simulation in BIM

2018 ◽  
Author(s):  
Madhumita Akhuli ◽  
Chunhee Cho ◽  
JeeWoong Park ◽  
Kyungki Kim
Keyword(s):  
Automated Generation ◽  
Discrete Action ◽  
Interaction Simulation

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

Automated generation of burnup chain for reactor analysis applications

Kerntechnik ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. 196-205
Author(s):  
V.-P. Tran ◽  
H.-N. Tran ◽  
A. Yamamoto ◽  
T. Endo
Keyword(s):  
Automated Generation

scholarly journals Responsive and Personalized Web Layouts with Integer Programming

2021 ◽  
Vol 5 (EICS) ◽  
pp. 1-23
Author(s):  
Markku Laine ◽  
Yu Zhang ◽  
Simo Santala ◽  
Jussi P. P. Jokinen ◽  
Antti Oulasvirta
Keyword(s):  
Integer Programming ◽  
Web Design ◽  
Web Pages ◽  
Web Page Design ◽  
Automated Generation ◽  
Novel Approach ◽  
Wide Range ◽  
Responsive Design ◽  
Responsive Web Design ◽  
Page Design

Over the past decade, responsive web design (RWD) has become the de facto standard for adapting web pages to a wide range of devices used for browsing. While RWD has improved the usability of web pages, it is not without drawbacks and limitations: designers and developers must manually design the web layouts for multiple screen sizes and implement associated adaptation rules, and its "one responsive design fits all" approach lacks support for personalization. This paper presents a novel approach for automated generation of responsive and personalized web layouts. Given an existing web page design and preferences related to design objectives, our integer programming -based optimizer generates a consistent set of web designs. Where relevant data is available, these can be further automatically personalized for the user and browsing device. The paper includes presentation of techniques for runtime adaptation of the designs generated into a fully responsive grid layout for web browsing. Results from our ratings-based online studies with end users (N = 86) and designers (N = 64) show that the proposed approach can automatically create high-quality responsive web layouts for a variety of real-world websites.

scholarly journals Variational Discrete Action Theory

2021 ◽  
Vol 126 (20) ◽  
Author(s):  
Zhengqian Cheng ◽  
Chris A. Marianetti
Keyword(s):  
Action Theory ◽  
Discrete Action

scholarly journals Interactive effects of incentive value and valence on the performance of discrete action sequences

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tyler J. Adkins ◽  
Bradley S. Gary ◽  
Taraz G. Lee
Keyword(s):  
Motor Learning ◽  
Large Body ◽  
Interactive Effects ◽  
Motor Tasks ◽  
Skill Retention ◽  
Incentive Value ◽  
Discrete Action ◽  
On Line ◽  
And Performance ◽  
Gains And Losses

AbstractIncentives can be used to increase motivation, leading to better learning and performance on skilled motor tasks. Prior work has shown that monetary punishments enhance on-line performance while equivalent monetary rewards enhance off-line skill retention. However, a large body of literature on loss aversion has shown that losses are treated as larger than equivalent gains. The divergence between the effects of punishments and reward on motor learning could be due to perceived differences in incentive value rather than valence per se. We test this hypothesis by manipulating incentive value and valence while participants trained to perform motor sequences. Consistent with our hypothesis, we found that large reward enhanced on-line performance but impaired the ability to retain the level of performance achieved during training. However, we also found that on-line performance was better with reward than punishment and that the effect of increasing incentive value was more linear with reward (small, medium, large) while the effect of value was more binary with punishment (large vs not large). These results suggest that there are differential effects of punishment and reward on motor learning and that these effects of valence are unlikely to be driven by differences in the subjective magnitude of gains and losses.

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