8,813 research outputs found
Een krachtige leeromgeving voor elke leerling
No full textAbstract: Het observeren van de eigen onderwijspraktijk vormt een waardevol vertrekpunt. Dat kan door middel van de observatiewijzer binnenklasdifferentiatie en de bijhorende gespreksleidraad, die Tom Smits en Els Tanghe naar aanleiding van hun onderzoek uit 2013-2015 en 2023 ontwikkelden. De toolbox met werkvormen voor binnenklasdifferentiatie en de toolbox diversiteitscompetenties kunnen bijkomende inspiratie bieden
Michael Rodriguez interviews author Tom Springer
No full textAuthor Tom Springer is interviewed about his writing career and his newest book "Looking for hickories". Springer talks about his career following after earning an Environmental Journalism degree from Michigan State University. He calls his genre "creative non-fiction" and explains how he weaves his memories into his books about life in rural and wild Michigan. Part of the Michigan State University Libraries' Michigan Writers Series. Springer is interviewed by Librarian Michael Rodriguez
Performing the archive: following in the footsteps
No full textUsing documentation of Mike Pearson's performance 'Bubbling Tom', Deirdre Heddon attempts to step into his shoes and re-perform it
CRE Author Tom Franklin
No full textCommon Reading Experience author and UM creative writing instructor Tom Franklin talks about his novel, Crooked Letter, Crooked Letter. Video by Mary Stanton.https://egrove.olemiss.edu/umvideo/1334/thumbnail.jp
Tom Kubancik
No full textTom is the Vice President of Advanced Programs at Applied Defense Solutions (ADS).
Tom’s entire career has been focused on advanced technology with over 30 years in Space Systems, High Performance Computing, and Microelectronics. With a background in Operations Management, Tom has enjoyed broad success when pioneering companies in rapidly evolving markets and shaping today’s high technology landscape.
Tom is a recognized international expert in Space Situational Awareness (SSA), participating in research, development, and deployment programs since the 1980’s. At Applied Defense Solutions (ADS), Tom has led the transition away from military-only SSA, establishing a broad portfolio of research and development, commercialization, and operational support programs. ADS is a recognized leader in civilian, commercial, and government space exploration, focusing on all phases from mission analysis, operations support, and space protection. Leading the ADS Advanced Programs’ team, Tom coordinates a highly talented group of technical experts working alongside program managers, operational experts, and capture professionals. Their focus is to create and develop opportunities for ADS to apply its innovations and expertise to the most challenging space systems development tasks. His team harnesses a company-wide passion for problem-solving by leveraging a world class research portfolio with exquisite analytical capabilities and deep operational experience. ADS has constructed the most interesting mission portfolio in the industry as Tom and his team love their role in defining the next generation of safe space operations.
Tom is an active participant in NATO Science and Technology panels and activities leading to better understanding of global approaches for effective coalition and collaborative SSA. Tom is a published author on global SSA and is a frequent speaker at domestic and international conferences.
Tom has a wealth of experience with leadership positions. He is a graduate of Bowling Green University. Tom and his family live in Boulder, Colorado.https://commons.erau.edu/stm-images/1097/thumbnail.jp
Tom Lawson
No full textTom Lawson is Professor of History and Pro Vice Chancellor for Arts, Design and Social Sciences at Northumbria University. He is the author and editor of several books including Debates on the Holocaust (2010) and most recently The Last Man: a British Genocide in Tasmania (2014).https://commons.erau.edu/genocide-bios/1044/thumbnail.jp
Genetic algorithm learning in a New Keynesian macroeconomic setup
No full textIn order to understand heterogeneous behavior amongst agents, empirical
data from Learning-to-Forecast (LtF) experiments can be used to construct learning
models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms
(GA) model to replicate the results from their LtF experiment. In this GA
model, individuals optimize an adaptive, a trend following and an anchor coefficient
in a population of general prediction heuristics. We replicate experimental treatments
in a New-Keynesian environment with increasing complexity and use Monte
Carlo simulations to investigate how well the model explains the experimental data.
We find that the evolutionary learning model is able to replicate the three different
types of behavior, i.e. convergence to steady state, stable oscillations and dampened
oscillations in the treatments using one GA model. Heterogeneous behavior can
thus be explained by an adaptive, anchor and trend extrapolating component and the
GA model can be used to explain heterogeneous behavior in LtF experiments with
different types of complexity
Compliance Update with Tom Fox
No full textJoin us for lunch with Tom Fox, compliance professional, author and creator of the Compliance Podcast Network, hosting a variety of compliance related podcasts, including a succinct daily compliance tip
First person – Tom Carruthers.
No full textFirst Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping early-career researchers promote themselves alongside their papers. Tom Carruthers is first author on ‘ exTREEmaTIME: a method for incorporating uncertainty into divergence time estimates’, published in BiO. Tom conducted the research described in this article while a PhD student in Professor Robert Scotland's lab in the Department of Plant Sciences, University of Oxford. He is now a postdoc in the lab of Dr William Baker at the Royal Botanic Gardens, Kew, working on determining the extent to which large molecular phylogenies provide information about evolutionary history
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