Local Dynamics of Stereoregular PMMAs Using Molecular Simulation

2002 ◽  
Vol 35 (3) ◽  
pp. 722-726 ◽  
Author(s):  
A. Soldera ◽  
Y. Grohens
Keyword(s):  
Molecular Simulation ◽  
Local Dynamics

A Donor Influenced by Local Dynamics

2016 ◽  
Vol 2 (1) ◽  
pp. 51-69 ◽  
Author(s):  
Setsuko Matsuzawa
Keyword(s):  
Poverty Alleviation ◽  
Rural Poverty ◽  
Unintended Consequences ◽  
Local Dynamics ◽  
Grant Program ◽  
Local Actors ◽  
Research Interviews ◽  
National Model ◽  
Management Project ◽  
Development Discourses

This article explores the relations between a foreign aid donor and local actors in the context of the dissemination of development discourses and practices in an authoritarian context. It addresses the question “To what extent may the local dynamics alter the original goals of a donor and lead to unintended consequences?” Based on archival research, interviews, and secondary literature, this case study examines the Yunnan Uplands Management Project (YUM) in 1990–95, the Ford Foundation's first grant program on rural poverty alleviation in China. While the Foundation did not attain its main goal of making YUM a national model for poverty alleviation, the local actors were able to use YUM to develop individual capacities and to build roles for themselves as development actors in the form of associations and nongovernmental organizations, resulting in further support from the Foundation. The study contributes to our understanding of donor-local actor dynamics by highlighting the gaps between the original goals of a donor and the perspectives and motivations of local actors. The study suggests that local dynamics may influence the goals of donors and the ways they seek to disseminate development discourses and practices to local actors, despite the common conception of donors as hegemonic or culturally imperialistic.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

SHORT-TERM DYNAMICS OF THE ANAPA BAY-BAR SHORELINE

2017 ◽  
Author(s):  
Marina Krylenko ◽  
Marina Krylenko ◽  
Alexandr Aleynikov ◽  
Alexandr Aleynikov ◽  
Viacheslav Krylenko ◽  
...  
Keyword(s):  
Black Sea ◽  
Satellite Images ◽  
The Black Sea ◽  
Northwestern Part ◽  
Short Term ◽  
Shoreline Position ◽  
Local Dynamics ◽  
Average Displacement ◽  
Local Erosion

The Anapa bay-bar is located in the northwestern part of the Black Sea. With the goal in mind to determine the short-term dynamics of the Anapa bay-bar we analyzed satellite images from 2003 to the present. Depending on the hydro-lithodynamical situation the shoreline configuration during storm can vary from a rectilinear to sinusoidal forms. There are regions of local erosion or accumulation whose formation is related to the alongshore motion of sediments and dynamics of underwater bars. Comparison of the data on 1965 and 1966 showed that in this period the amplitude of the shoreline position was more than 20 m but average displacement of the shoreline for 13 months was only 0.8 m. This study showed that for the analysis of changes in the shoreline position is necessary to consider the configuration of the coastline at the time of each observation and the local dynamics.

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