1,693 research outputs found
Unsupervised landmark analysis for jump detection in molecular dynamics simulations
Molecular dynamics is a versatile and powerful method to study diffusion in
solid-state ionic conductors, requiring minimal prior knowledge of equilibrium
or transition states of the system's free energy surface. However, the analysis
of trajectories for relevant but rare events, such as a jump of the diffusing
mobile ion, is still rather cumbersome, requiring prior knowledge of the
diffusive process in order to get meaningful results. In this work, we present
a novel approach to detect the relevant events in a diffusive system without
assuming prior information regarding the underlying process. We start from a
projection of the atomic coordinates into a landmark basis to identify the
dominant features in a mobile ion's environment. Subsequent clustering in
landmark space enables a discretization of any trajectory into a sequence of
distinct states. As a final step, the use of the smooth overlap of atomic
positions descriptor allows distinguishing between different environments in a
straightforward way. We apply this algorithm to ten Li-ionic systems and
conduct in-depth analyses of cubic LiLaZrO, tetragonal
LiGePS, and the -eucryptite LiAlSiO. We
compare our results to existing methods, underscoring strong points,
weaknesses, and insights into the diffusive behavior of the ionic conduction in
the materials investigated
Efficient deep data assimilation with sparse observations and time-varying sensors
Variational Data Assimilation (DA) has been broadly used in engineering
problems for field reconstruction and prediction by performing a weighted
combination of multiple sources of noisy data. In recent years, the integration
of deep learning (DL) techniques in DA has shown promise in improving the
efficiency and accuracy in high-dimensional dynamical systems. Nevertheless,
existing deep DA approaches face difficulties in dealing with unstructured
observation data, especially when the placement and number of sensors are
dynamic over time. We introduce a novel variational DA scheme, named
Voronoi-tessellation Inverse operator for VariatIonal Data assimilation
(VIVID), that incorporates a DL inverse operator into the assimilation
objective function. By leveraging the capabilities of the Voronoi-tessellation
and convolutional neural networks, VIVID is adept at handling sparse,
unstructured, and time-varying sensor data. Furthermore, the incorporation of
the DL inverse operator establishes a direct link between observation and state
space, leading to a reduction in the number of minimization steps required for
DA. Additionally, VIVID can be seamlessly integrated with Proper Orthogonal
Decomposition (POD) to develop an end-to-end reduced-order DA scheme, which can
further expedite field reconstruction. Numerical experiments in a fluid
dynamics system demonstrate that VIVID can significantly outperform existing DA
and DL algorithms. The robustness of VIVID is also accessed through the
application of various levels of prior error, the utilization of varying
numbers of sensors, and the misspecification of error covariance in DA
Structure of plastically compacting granular packings
The developing structure in systems of compacting ductile grains were studied
experimentally in two and three dimensions. In both dimensions, the peaks of
the radial distribution function were reduced, broadened, and shifted compared
with those observed in hard disk- and sphere systems. The geometrical
three--grain configurations contributing to the second peak in the radial
distribution function showed few but interesting differences between the
initial and final stages of the two dimensional compaction. The evolution of
the average coordination number as function of packing fraction is compared
with other experimental and numerical results from the literature. We conclude
that compaction history is important for the evolution of the structure of
compacting granular systems.Comment: 12 pages, 12 figure
Developing Algorithms for Quantifying the Super Resolution Microscopic Data: Applications to the Quantification of Protein-Reorganization in Bacteria Responding to Treatment by Silver Ions
Histone-like nucleoid structuring proteins (HNS) play significant roles in shaping the chromosomal DNA, regulation of transcriptional networks in microbes, as well as bacterial responses to environmental changes such as temperature fluctuations. In this work, the intracellular organization of HNS proteins in E. coli bacteria was investigated utilizing super-resolution fluorescence microscopy, which surpasses conventional microscopy by 10–20 fold in spatial resolution. More importantly, the changes of the spatial distribution of HNS proteins in E. coli, by addition of silver ions into the growth medium were explored. To quantify the spatial distribution of HNS in bacteria and its changes, an automatic method based on Voronoi diagram was implemented. The HNS proteins localized in super-resolution fluorescence microscopy were segmented and clustered based on several quantitative parameters, such as molecular areas, molecular densities, and mean inter-molecular distances of the k-th rank, all of which were computed from the Voronoi diagrams. These parameters, as well as the associated clustering analysis, allowed us to quantify how the spatial organization of HNS proteins responds to silver, and provided insight into understanding how microbes adapt to new environments
Geospatial Tessellation in the Agent-In-Cell Model: A Framework for Agent-Based Modeling of Pandemic
Agent-based simulation is a versatile and potent computational modeling
technique employed to analyze intricate systems and phenomena spanning diverse
fields. However, due to their computational intensity, agent-based models
become more resource-demanding when geographic considerations are introduced.
This study delves into diverse strategies for crafting a series of Agent-Based
Models, named "agent-in-the-cell," which emulate a city. These models,
incorporating geographical attributes of the city and employing real-world
open-source mobility data from Safegraph's publicly available dataset, simulate
the dynamics of COVID spread under varying scenarios. The "agent-in-the-cell"
concept designates that our representative agents, called meta-agents, are
linked to specific home cells in the city's tessellation. We scrutinize
tessellations of the mobility map with varying complexities and experiment with
the agent density, ranging from matching the actual population to reducing the
number of (meta-) agents for computational efficiency. Our findings demonstrate
that tessellations constructed according to the Voronoi Diagram of specific
location types on the street network better preserve dynamics compared to
Census Block Group tessellations and better than Euclidean-based tessellations.
Furthermore, the Voronoi Diagram tessellation and also a hybrid -- Voronoi
Diagram - and Census Block Group - based -- tessellation require fewer
meta-agents to adequately approximate full-scale dynamics. Our analysis spans a
range of city sizes in the United States, encompassing small (Santa Fe, NM),
medium (Seattle, WA), and large (Chicago, IL) urban areas. This examination
also provides valuable insights into the effects of agent count reduction,
varying sensitivity metrics, and the influence of city-specific factors
Fluid dynamics and mass transfer in porous media: Modelling fluid flow and filtration inside open-cell foams
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