MO-F-CAMPUS-T-03: Data Driven Approaches for Determination of Treatment Table Tolerance Values for Record and Verification Systems

2015 ◽  
Vol 42 (6Part29) ◽  
pp. 3572-3573 ◽  
Author(s):  
N Gupta ◽  
D DiCostanzo ◽  
M Fullenkamp
Keyword(s):  
Data Driven ◽  
Verification Systems ◽  
Treatment Table

Data-driven modeling for determination of asphaltene stability condition in oil system

2018 ◽  
Vol 36 (11) ◽  
pp. 726-731
Author(s):  
Arash Kamari ◽  
Amir H. Mohammadi ◽  
Deresh Ramjugernath
Keyword(s):  
Stability Condition ◽  
Data Driven ◽  
Data Driven Modeling

scholarly journals Determination of Varying Group Sizes for Pooling Procedure

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Wenjun Xiong ◽  
Hongyu Lu ◽  
Juan Ding
Keyword(s):  
Group Size ◽  
Rare Diseases ◽  
Population Heterogeneity ◽  
Sequential Procedure ◽  
Data Driven ◽  
Equal Group ◽  
Essential Step ◽  
Available Information ◽  
Individual Information

Pooling is an attractive strategy in screening infected specimens, especially for rare diseases. An essential step of performing the pooled test is to determine the group size. Sometimes, equal group size is not appropriate due to population heterogeneity. In this case, varying group sizes are preferred and could be determined while individual information is available. In this study, we propose a sequential procedure to determine varying group sizes through fully utilizing available information. This procedure is data driven. Simulations show that it has good performance in estimating parameters.

scholarly journals Computational modelling of the inflammatory response in trauma, sepsis and wound healing: implications for modelling resilience

2014 ◽  
Vol 4 (5) ◽  
pp. 20140004 ◽  
Author(s):  
Yoram Vodovotz
Keyword(s):  
Inflammatory Response ◽  
Acute Inflammation ◽  
Computational Modelling ◽  
Data Driven ◽  
Patient Specific ◽  
Cell Tissue ◽  
Organ Systems ◽  
Forward Process

Resilience refers to the ability to recover from illness or adversity. At the cell, tissue, organ and whole-organism levels, the response to perturbations such as infections and injury involves the acute inflammatory response, which in turn is connected to and controlled by changes in physiology across all organ systems. When coordinated properly, inflammation can lead to the clearance of infection and healing of damaged tissues. However, when either overly or insufficiently robust, inflammation can drive further cell stress, tissue damage, organ dysfunction and death through a feed-forward process of inflammation → damage → inflammation. To address this complexity, we have obtained extensive datasets regarding the dynamics of inflammation in cells, animals and patients, and created data-driven and mechanistic computational simulations of inflammation and its recursive effects on tissue, organ and whole-organism (patho)physiology. Through this approach, we have discerned key regulatory mechanisms, recapitulated in silico key features of clinical trials for acute inflammation and captured diverse, patient-specific outcomes. These insights may allow for the determination of individual-specific tolerances to illness and adversity, thereby defining the role of inflammation in resilience.

scholarly journals Agile Software Development for Housing Reconstruction: The 2017 Earthquake case in Mexico

2021 ◽  
Vol 33 (5) ◽  
pp. 219-236
Author(s):  
Javier Ortiz-Hernández ◽  
Víctor Josue Ruiz-Martínez ◽  
María Yasmín Hernández-Pérez ◽  
Rito Mijarez-Castro
Keyword(s):  
Emergency Services ◽  
Open Data ◽  
Lessons Learned ◽  
Data Driven ◽  
Package Delivery ◽  
Richter Scale ◽  
The Government ◽  
Housing Reconstruction ◽  
Agile Software

On September 19, 2017 an earthquake occurred in Mexico with epicenter in the limits of the states of Puebla and Morelos. It had a magnitude of 7.1 on the Richter scale. 31,090 homes were affected, of which 1659 with total damage. In order to attend to the contingency, the government of Morelos formed an inter-institutional committee in the first hours to carry out an initial diagnosis of the damage and to provide emergency services. This article presents a case study and lessons learned from the software engineering support for the development of a data-driven platform in the various phases of contingency response: census of damaged homes, identification of aid beneficiaries, determination of aid packages according to a damage assessment, logistics and follow-up of aid package delivery, data-driven decision making, and a public portal for open data and budget transparency. KEYWORDS

3D wavefront attribute determination and conflicting dip processing

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. V325-V343 ◽  
Author(s):  
Yujiang Xie ◽  
Dirk Gajewski
Keyword(s):  
Decomposition Method ◽  
Data Driven ◽  
Superior Performance ◽  
Pragmatic Approach ◽  
Swarm Optimization ◽  
Model Driven ◽  
Common Reflection Surface ◽  
The Common ◽  
Dip Angle

The knowledge of 3D wavefront attributes allows many important applications, such as stacking, 5D interpolation, 3D diffraction separation and imaging, and 3D wavefront tomography, just to name a few. For the determination of wavefront attributes, we use the common-reflection-surface (CRS) operator. We adopt a simultaneous search for the determination of wavefront attributes and combine it with conflicting dip processing. For the simultaneous search, we compare three heuristic global optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE). For conflicting dip processing, a dip angle decomposition method for the probed sample is introduced and the simultaneous search is independently performed in specified dip ranges to individually obtain attributes and semblance for each range. Results for the laterally heterogeneous 3D SEG C3WA data indicate that DE has superior performance to determine the 3D wavefront attributes when compared with PSO, GA, and the conventional pragmatic approach because a higher semblance and an improved set of wavefront attributes are achieved. A comparison of the data-driven wavefront attributes obtained from the DE with the model-driven wavefront attributes computed by kinematic and dynamic ray tracing reveals the validity of the data-driven wavefront attributes. Combining the simultaneous search with conflicting dip processing for the 3D CRS stack further improved reflected energy and diffraction details when compared with results without simultaneous search and/or conflicting dip processing.

scholarly journals Data-Driven Critical Tract Variable Determination for European Portuguese

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 491
Author(s):  
Samuel Silva ◽  
Nuno Almeida ◽  
Conceição Cunha ◽  
Arun Joseph ◽  
Jens Frahm ◽  
...  
Keyword(s):  
Speech Synthesis ◽  
Visual Analysis ◽  
Vocal Tract ◽  
Data Driven ◽  
Imaging Data ◽  
Articulatory Phonology ◽  
Critical Variable ◽  
European Portuguese

Technologies, such as real-time magnetic resonance (RT-MRI), can provide valuable information to evolve our understanding of the static and dynamic aspects of speech by contributing to the determination of which articulators are essential (critical) in producing specific sounds and how (gestures). While a visual analysis and comparison of imaging data or vocal tract profiles can already provide relevant findings, the sheer amount of available data demands and can strongly profit from unsupervised data-driven approaches. Recent work, in this regard, has asserted the possibility of determining critical articulators from RT-MRI data by considering a representation of vocal tract configurations based on landmarks placed on the tongue, lips, and velum, yielding meaningful results for European Portuguese (EP). Advancing this previous work to obtain a characterization of EP sounds grounded on Articulatory Phonology, important to explore critical gestures and advance, for example, articulatory speech synthesis, entails the consideration of a novel set of tract variables. To this end, this article explores critical variable determination considering a vocal tract representation aligned with Articulatory Phonology and the Task Dynamics framework. The overall results, obtained considering data for three EP speakers, show the applicability of this approach and are consistent with existing descriptions of EP sounds.

scholarly journals Conditional expectation estimation through attributable components

2018 ◽  
Vol 7 (4) ◽  
pp. 727-754 ◽  
Author(s):  
Esteban G Tabak ◽  
Giulio Trigila
Keyword(s):  
Conditional Distribution ◽  
General Framework ◽  
Optimal Transport ◽  
Data Driven ◽  
Categorical Variables ◽  
Order Structure ◽  
One Dimensional ◽  
Conditional Mean ◽  
Full Conditional Distribution

Abstract A general methodology is proposed for the explanation of variability in a quantity of interest x in terms of covariates z = (z1, …, zL). It provides the conditional mean $\bar{x}(z)$ as a sum of components, where each component is represented as a product of non-parametric one-dimensional functions of each covariate zl that are computed through an alternating projection procedure. Both x and the zl can be real or categorical variables; in addition, some or all values of each zl can be unknown, providing a general framework for multi-clustering, classification and covariate imputation in the presence of confounding factors. The procedure can be considered as a preconditioning step for the more general determination of the full conditional distribution $\boldsymbol{\rho}(x|z) $ through a data-driven optimal-transport barycenter problem. In particular, just iterating the procedure once yields the second order structure (i.e. the covariance) of $\boldsymbol{\rho}(x|z) $. The methodology is illustrated through examples that include the explanation of variability of ground temperature across the continental United States and the prediction of book preference among potential readers.

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