Challenges of Pre-Salt Imaging in Brazil’s Santos Basin - A Case Study on a Variable-Depth Streamer Data Set

Chapter 7 begins with an overview of Erikson’s ideas about intimacy and its place in the life cycle, followed by a summary of Bowlby and Ainsworth’s attachment theory framework and its relation to family development. The authors review existing longitudinal research on the development of family relationships in adolescence and emerging adulthood, focusing on evidence with regard to links to McAdams and Pals’ personality model. They discuss the evidence, both questionnaire and narrative, from the Futures Study data set on family relationships, including emerging adults’ relations with parents and, separately, with grandparents, as well as their anticipations of their own parenthood. As a way of illustrating the key personality concepts from this family chapter, the authors end with a case study of Jane Fonda in youth and her father, Henry Fonda, to illustrate these issues through the lives of a 20th-century Hollywood dynasty of actors.

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Vocational Development in Emerging Adulthood

10.1093/oso/9780199934263.003.0006 ◽

2018 ◽

Author(s):

Michael W. Pratt ◽

M. Kyle Matsuba

Keyword(s):

Turning Point ◽

Life Stories ◽

Life Story ◽

Emerging Adult ◽

Vocational Development ◽

Data Set ◽

The World ◽

Key Concepts

Chapter 6 reviews research on the topic of vocational/occupational development in relation to the McAdams and Pals tripartite personality framework of traits, goals, and life stories. Distinctions between types of motivations for the work role (as a job, career, or calling) are particularly highlighted. The authors then turn to research from the Futures Study on work motivations and their links to personality traits, identity, generativity, and the life story, drawing on analyses and quotes from the data set. To illustrate the key concepts from this vocation chapter, the authors end with a case study on Charles Darwin’s pivotal turning point, his round-the-world voyage as naturalist for the HMS Beagle. Darwin was an emerging adult in his 20s at the time, and we highlight the role of this journey as a turning point in his adult vocational development.

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How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

Methods of Information in Medicine ◽

10.1055/s-0038-1634384 ◽

2003 ◽

Vol 42 (05) ◽

pp. 564-571 ◽

Cited By ~ 23

Author(s):

M. Schumacher ◽

E. Graf ◽

T. Gerds

Keyword(s):

Test Data ◽

Survival Data ◽

Prediction Error ◽

Classification Scheme ◽

Neural Nets ◽

Brier Score ◽

Data Set ◽

Independent Test ◽

Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

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Magnetotelluric static shift: Estimation and removal using the cokriging method

Geophysics ◽

10.1190/1.2400625 ◽

2007 ◽

Vol 72 (1) ◽

pp. F25-F34 ◽

Cited By ~ 11

Author(s):

Benoit Tournerie ◽

Michel Chouteau ◽

Denis Marcotte

Keyword(s):

Apparent Resistivity ◽

A Priori ◽

Shift Factor ◽

Static Shift ◽

Geostatistical Model ◽

Data Set ◽

Resistivity Sounding ◽

The Difference ◽

Cross Variogram

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

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An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.26-28.620 ◽

2010 ◽

Vol 26-28 ◽

pp. 620-624 ◽

Cited By ~ 3

Author(s):

Zhan Wei Du ◽

Yong Jian Yang ◽

Yong Xiong Sun ◽

Chi Jun Zhang ◽

Tuan Liang Li

Keyword(s):

Genetic Algorithm ◽

Ant Colony Algorithm ◽

Ant Colony ◽

Convergence Time ◽

Ant Colony Optimization Algorithm ◽

Data Set ◽

Research Attention ◽

Simply Supported ◽

Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

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