Investigating Nonlinearity: A Note on the Estimation of Hamilton’s Random Field Regression Model

This is a revised and extended version of the authors’ 2003 Trinity Economic Paper. It describes Hamilton’s (2001) approach to nonlinear econometric modelling and some of the methods of nonlinear optimization, as before, but adds significantly to the investigation of Hamilton’s Gauss program for the implementation of his methodology. Specifically, it reports on the performance of this program using data relating to Hamilton’s US Phillips curve example, the use of two versions of the Gauss software and a range of numerical optimization options. It also examines the impact of changes in initial parameter estimates, the use of algorithm switching strategies, and the e?ects of changes in the sample data on the results produced by Hamilton’s procedure. The new results presented suggest some further clear conclusions that will be of value to those using Hamilton’s method.

A micro-mechanics damage approach for fatigue of composite materials

A new model for fatigue damage evolution of polymer matrix composites is presented. The model is based on a combination of an orthotropic damage model and an isotropic fatigue evolution model. The orthotropic damage model is used to predict the orthotropic damage evolution within a single cycle. The isotropic fatigue model is used to predict the magnitude of fatigue damage accumulated as a function of the number of cycles. This approach facilitates the determination of model parameters since the orthotropic damage model parameters can be determined from available data from quasi-static loading tests. Then, limited amount of fatigue data is needed to adjust the fatigue evolution model. The combination of these two models provides a compromise between efficiency and accuracy. Decomposition of the state variables down to the constituent scale is accomplished by micro-mechanics. Phenomenological damage evolution models are then postulated for each constituent and for the micro-structural interaction among them. Model parameters are determined from available experimental data. Comparison between model predictions and additional experimental data is presented

Proceedings of the OHBM Brainhack 2021

The global pandemic presented new challenges and op-portunities for organizing conferences, and OHBM 2021was no exception. The OHBM Brainhack is an event thatoccurs just prior to the OHBM meeting, typically in-per-son, where scientists of all levels of expertise and interestgather to work and learn together for a few days in a col-laborative hacking-style environment on projects of com-mon interest (1). Building off the success of the OHBM2020 Hackathon (2), the 2021 Open Science SpecialInterest Group came together online to organize a largecoordinated Brainhack event that would take place overthe course of 4 days. The OHBM 2021 Brainhack eventwas organized along two guiding principles, providinga highly inclusive collaborative environment for inter-action between scientists across disciplines and levelsof expertise to push forward important projects thatneed support, also known as the “Hack-Track” of theBrainhack. The second aim of the OHBM Brainhack is toempower scientists to improve the quality of their sci-entific endeavors by providing high-quality hands-ontraining on best practices in open-science approaches.This is best exemplified by the training events providedby the “Train-Track” at the OHBM 2021 Brainhack. Here,we briefly explain both of these elements of the OHBM2021 Brainhack, before continuing on to the Brainhackproceedings