scholarly journals A Diffeomorphic Aging Model for Adult Human Brain from Cross-Sectional Data

2021 ◽  
Author(s):  
Alphin J. Thottupattu ◽  
Jayanthi Sivaswamy ◽  
Venkateswaran P. Krishnan
Keyword(s):  
Longitudinal Data ◽  
Natural Aging ◽  
Image Data ◽  
Cross Sectional ◽  
Aging Model ◽  
Time Points ◽  
Proposed Model ◽  
Structural Disorders ◽  
Structural Template ◽  
Diffeomorphic Deformation

Abstract Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data – follow-up data of the same subject over different time points. In practice, obtaining such longitudinal data is difficult. We propose a method to develop an aging model for a given population, in the absence of longitudinal data, by using images from different subjects at different time points, the so-called cross-sectional data. We define an aging model as a diffeomorphic deformation on a structural template derived from the data and propose a method that develops topology preserving aging model close to natural aging. The proposed model is successfully validated on two public cross-sectional datasets which provide templates constructed from different sets of subjects at different age points.

Automated Diagonal Slice and View Solution for 3D Device Structure Analysis

2018 ◽  
Author(s):  
Sang Hoon Lee ◽  
Jeff Blackwood ◽  
Stacey Stone ◽  
Michael Schmidt ◽  
Mark Williamson ◽  
...  
Keyword(s):  
Cross Section ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Image Data ◽  
Planar Surface ◽  
Device Structure ◽  
Cross Sectional ◽  
Device Structures ◽  
The Cross ◽  
Planar Analysis

Abstract The cross-sectional and planar analysis of current generation 3D device structures can be analyzed using a single Focused Ion Beam (FIB) mill. This is achieved using a diagonal milling technique that exposes a multilayer planar surface as well as the cross-section. this provides image data allowing for an efficient method to monitor the fabrication process and find device design errors. This process saves tremendous sample-to-data time, decreasing it from days to hours while still providing precise defect and structure data.

scholarly journals Determinants of revenue management practices and their impacts on the financial performance of hotels in Kenya: a proposed theoretical framework

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael Murimi ◽  
Billy Wadongo ◽  
Tom Olielo
Keyword(s):  
Financial Performance ◽  
Revenue Management ◽  
Management Practices ◽  
Theoretical Framework ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Second Stage ◽  
Theoretical Structure ◽  
Proposed Model ◽  
Conceptual Paper

AbstractThis conceptual paper aims at identifying a theoretical framework for the determinants of revenue management (RM) practices and their impacts on the financial performance of hotels. To create this framework, a two-phased process is employed where the first stage involves an explicit examination of the literature related to practices of revenue management and their determinants and to hotel financial performance. The second stage involves an enhancement of the framework. The theoretical structure is developed based on past theoretical explanations, and empirical analysis is conducted in the fields of revenue management. The researchers propose a theoretical framework illustrating how revenue management practices and their determinants affect the financial performance of Kenyan hotels. The use of contingency theory and its justifications and inadequacies among studies on revenue management in hotels is highlighted. The methods highlighted by the reviewed theoretical framework may be utilized to organize revenue management (RM) practices and their determinants for Kenyan hotels. Measurements for the financial performance of hotels are also described. Last, the researchers call for empirical research that authenticates the proposed model using a cross-sectional survey. The present work can inspire scholars and specialists to determine how RM practices and their determinants impact the financial performance of hotels. By assimilating knowledge from numerous disciplines, this paper emphasizes aggregated awareness surrounding the conceptualization of RM, RM practices adopted in hotels, and the financial performance of hotels.

scholarly journals E-bike user groups and substitution effects: evidence from longitudinal travel data in the Netherlands

2021 ◽  
Author(s):  
Mathijs de Haas ◽  
Maarten Kroesen ◽  
Caspar Chorus ◽  
Sascha Hoogendoorn-Lanser ◽  
Serge Hoogendoorn
Keyword(s):  
The Netherlands ◽  
Longitudinal Data ◽  
Latent Class ◽  
Good Alternative ◽  
Substitution Effects ◽  
Or Education ◽  
Cross Sectional ◽  
User Groups ◽  
Using Data ◽  
Mode Of Transport

AbstractIn recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.

Component analysis in cross-sectional and longitudinal data

Psychometrika ◽  
1988 ◽  
Vol 53 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Roger E. Millsap ◽  
William Meredith
Keyword(s):  
Longitudinal Data ◽  
Component Analysis ◽  
Cross Sectional

P3-132: Motivational reserve as risk factor in the development of mild cognitive impairment and Alzheimer's disease: Cross-sectional and longitudinal data

2009 ◽  
Vol 5 (4S_Part_13) ◽  
pp. P383-P383
Author(s):  
Simon Forstmeier ◽  
Michael Wagner ◽  
Wolfgang Maier ◽  
Hendrik Van Den Bussche ◽  
Birgitt Wiese ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Factor ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Longitudinal Data ◽  
Cross Sectional

scholarly journals Deep learning-based estimation of Flory–Huggins parameter of A–B block copolymers from cross-sectional images of phase-separated structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katsumi Hagita ◽  
Takeshi Aoyagi ◽  
Yuto Abe ◽  
Shinya Genda ◽  
Takashi Honda
Keyword(s):  
Deep Learning ◽  
Diblock Copolymers ◽  
Three Dimensional ◽  
Image Data ◽  
Real Space ◽  
Cross Sectional ◽  
Regression Problem ◽  
Self Consistent Field ◽  
Experimental Image ◽  
Chain Lengths

AbstractIn this study, deep learning (DL)-based estimation of the Flory–Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25–40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

USE OF STRAIN GRADIENT THEORY FOR MODELING THE SIZE-DEPENDENT PULL-IN OF ROTATIONAL NANO-MIRROR IN THE PRESENCE OF MOLECULAR FORCE

2013 ◽  
Vol 27 (18) ◽  
pp. 1350083 ◽  
Author(s):  
Y. TADI BENI ◽  
M. ABADYAN
Keyword(s):  
Strain Gradient ◽  
Continuum Theory ◽  
Strain Gradient Theory ◽  
Gradient Theory ◽  
Cross Sectional ◽  
Size Dependent ◽  
Proposed Model ◽  
Molecular Force ◽  
Intrinsic Material Length ◽  
Material Length

Experiments reveal that mechanical behavior of nanostructures is size-dependent. Herein, the size dependent pull-in instability of torsional nano-mirror is investigated using strain gradient nonclassic continuum theory. The governing equation of the mirror is derived taking the effect of electrostatic Coulomb and molecular van der Waals (vdW) forces into account. Variation of the rotation angle of the mirror as a function of the applied voltage is obtained and the instability parameters i.e., pull-in voltage and pull-in angle are determined. Nano-mirrors with square and circular cross-sectional beams are investigated as case studies. It is found that when the thickness of the torsional nano-beam is comparable with the intrinsic material length scales, size effect can substantially increase the instability parameters of the rotational mirror. Moreover, the effect of vdW forces on the size-dependent pull-in instability of the system is discussed. The proposed model is able to predict the experimental results more accurately than the previous classic models and reduce the gap between experiment and previous theories.

Regression analysis of longitudinal covariates with censored and longitudinal outcome

2018 ◽  
Author(s):  
◽  
Li Chen
Keyword(s):  
Regression Analysis ◽  
Longitudinal Data ◽  
Linear Models ◽  
Partial Likelihood ◽  
Response Variable ◽  
Additive Hazards ◽  
Time Points ◽  
Longitudinal Outcome ◽  
Longitudinal Covariates ◽  
Weighting Technique

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Longitudinal data contain repeated measurements of variables on the same experimental subject. It is often of interest to analyze the relationship between these variables. Typically, there is one or several longitudinal covariates and a response variable that can be either longitudinal or time to an event. Regression models can be employed to analyze these relationships. Ideally, longitudinal variables should be continuously monitored and their complete trajectories along the time are observed. Practically, however, this is unrealistic, either economically or methodologically. Often one only obtains so called sparse longitudinal data, where variables are intermittently observed at relatively sparse time points within the period of study. Such sparse longitudinal data give rise to an issue for the analysis of the response of time to an event, where survival analysis is typically implemented, e.g. the Cox model or additive hazards model. In both models, the values of covariates of all subjects at risk are needed in order to calculate the partial likelihood. But in the case of sparse longitudinal data, the availability of these observations may not be satis fied. Moreover, if the response variable is also longitudinal, it is possible that the response and covariates are not observed altogether, or at least not close to each other enough to be considered as observed simultaneously. Although a wealth of studies have been dedicated to longitudinal data analysis, very few of them have seriously considered and rigorously studied the situation aforementioned. In this dissertation, we discuss the regression analysis of longitudinal cavities with censored and longitudinal outcome. To be specific, Chapter 2 targets the additive hazards models with sparse longitudinal covariates, Chapter 3 studies the partially linear models with longitudinal covariates and response observed at mismatched time points, also known as asynchronous longitudinal data, and Chapter 4 explores longitudinal data with more complex structures with linear models. Kernel weighting technique is the key idea to all the stated researches. Estimators are derived based on kernel weighting technique and their asymptotical properties were rigorously examined, along with simulation studies for their fi nite sample performance, and illustrations using real data sets.

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