Risk attitudes, job mobility and subsequent wage growth during the early career

Job change is a decision under uncertainty: It is associated with costs whereas the decision is made without full knowledge about future benefits. In order to investigate the relationship between willingness to take risks and job mobility, we first extend a model for on-the-job search with nonwage job characteristics by including heterogeneity in risk attitudes. Second, we empirically test the model's implications showing that individuals who are more risk-averse choose to change their jobs less often than more risk-tolerant individuals. This difference in the job changing behaviour leads to only moderate differences wage growth during early career: Risk-averse individuals tend to have on average higher wage gains from each job change and have obtained higher overall wage growth at the end of the early career phase

The migration and attrition behaviour among Hungarian radiographers

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Studies on the disbonding initiation of interfacial cracks.

With the continuing trend of decreasing feature sizes in flip-chip assemblies, the reliability tolerance to interfacial flaws is also decreasing. Small-scale disbonds will become more of a concern, pointing to the need for a better understanding of the initiation stage of interfacial delamination. With most accepted adhesion metric methodologies tailored to predict failure under the prior existence of a disbond, the study of the initiation phenomenon is open to development and standardization of new testing procedures. Traditional fracture mechanics approaches are not suitable, as the mathematics assume failure to originate at a disbond or crack tip. Disbond initiation is believed to first occur at free edges and corners, which act as high stress concentration sites and exhibit singular stresses similar to a crack tip, though less severe in intensity. As such, a 'fracture mechanics-like' approach may be employed which defines a material parameter--a critical stress intensity factor (K{sub c})--that can be used to predict when initiation of a disbond at an interface will occur. The factors affecting the adhesion of underfill/polyimide interfaces relevant to flip-chip assemblies were investigated in this study. The study consisted of two distinct parts: a comparison of the initiation and propagation phenomena and a comparison of the relationship between sub-critical and critical initiation of interfacial failure. The initiation of underfill interfacial failure was studied by characterizing failure at a free-edge with a critical stress intensity factor. In comparison with the interfacial fracture toughness testing, it was shown that a good correlation exists between the initiation and propagation of interfacial failures. Such a correlation justifies the continuing use of fracture mechanics to predict the reliability of flip-chip packages. The second aspect of the research involved fatigue testing of tensile butt joint specimens to determine lifetimes at sub-critical load levels. The results display an interfacial strength ranking similar to that observed during monotonic testing. The fatigue results indicate that monotonic fracture mechanics testing may be an adequate screening tool to help predict cyclic underfill failure; however lifetime data is required to predict reliability

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual diferences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantifed by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identifed previously unreported neurodevelo pmental milestones3 , showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological diferences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantifcation of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes