Knowing your AMS system's limits: system acceptance region exploration by using automated model refinement and accelerated simulation

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

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Leadership Enactment for Information System Acceptance: an Explanation from a Sensemaking Perspective

Sri Lankan Journal of Management ◽

10.33939/sljm.24.02.04.2019 ◽

2019 ◽

pp. 78-106

Author(s):

Aruna Dayanatha ◽

J A S K Jayakody

Keyword(s):

Information System ◽

User Involvement ◽

High Rate ◽

Is Success ◽

Managerial Leadership ◽

Individual Level ◽

System Acceptance ◽

The Individual ◽

Leadership Intervention ◽

Is Failure

Information system (IS) projects have been seen to be failing at an alarmingly high rate. The prevailing explanations of IS failure have had only a limited success. Thus, the time may be right to look at the reasons for IS failure through an alternative perspective. This paper proposes that IS success should be explained in terms of managerial leadership intervention, from the sensemaking perspective. Managers are responsible for workplace outcomes; thus, it may be appropriate to explain their role in IS success as well. The sensemaking perspective can explain IS success through holistic user involvement, a concept which critiques of existing explanations have stated to be a requirement for explaining IS failure. This paper proposes a framework combining the theory of enactment and leadership enactment to theorize managerial leadership intervention for “IS success.” The proposed explanation postulates that the managerial leader’s envisioning of the future transaction set influences the liberation of the follower and cast enactment, while liberating followers and cast enactment constitute manager sensegiving. The managerial leader’s sense-giving influences follower sensemaking. Follower sensemaking, under the influence of managerial sensegiving, will lead to followers’ IS acceptance, and that constitutes IS success at the individual level. Further, collective level IS acceptance constitutes IS adaption/success, and this will influence the leader’s sensegiving, for the next round of sensemaking.

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Accelerated Simulation of Kinetic Transport Using Variational Principles and Sparsity

10.2172/1417993 ◽

2017 ◽

Author(s):

Russel Caflisch

Keyword(s):

Variational Principles ◽

Accelerated Simulation

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Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3235-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3235-3292 ◽

Cited By ~ 5

Author(s):

A. L. Atchley ◽

S. L. Painter ◽

D. R. Harp ◽

E. T. Coon ◽

C. J. Wilson ◽

...

Keyword(s):

Carbon Sink ◽

Thermal Response ◽

Field Measurements ◽

Early Summer ◽

Active Layer Thickness ◽

Earth System ◽

Model Refinement ◽

System Models ◽

Developed Surface ◽

Earth System Models

Abstract. Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

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