Students' understanding of gravity using the rubber sheet analogy: an Italian experience

General Relativity (GR) represents the most recent theory of gravity, on which all modern astrophysics is based, including some of the most astonishing results of physics research. Nevertheless, its study is limited to university courses, while being ignored at high school level. To introduceGR in high school one of the approaches that can be used is the so-called rubber sheet analogy, i.e. comparing the space-time to a rubber sheet which deforms under a weight. In this paper we analyze the efficacy of an activity for high school students held at the Department of Mathematics and Physics of Roma Tre University that adopts the rubber sheet analogy to address several topics related to gravity. We present the results of the questionnaires we administered to investigate the understanding of the topics treated to over 150 Italian high school students who participated in this activity.Comment: 19 pages, 14 figure

Global recession and higher education in eastern Asia: China, Mongolia and Vietnam

This paper presents a perspective on the capacity of colleges and universities during past and present economic shocks. The main argument is that the environment of the global recession-an Asia far more economically integrated than during past economic shocks, with more unified aspirations to be globally competitive and socially responsible-no longer delay reforms in higher education. In fact, the global recession has become an opportune time for higher education in Asia, specifically developing countries in eastern (East and Southeast) Asia, to continue reforming governance and administration, access and equity, internal and external efficiency, and regional collaboration. Economic shocks have accelerated reforms in higher education, especially those for promoting innovation in their economies, though more is needed in improving governance and access for underserved populations. This paper examines the cases of China, Mongolia, and Vietnam as examples of how the global recession and regional integration are growing forces in shaping their higher education reform and development. The paper also identifies a series of measures for increasing the resilience of higher education systems in serving poor and vulnerable populations during economic recessions. Responses to the global economic recession by nations in eastern Asia are likely to improve the global shift in economy and human capital. © 2011 The Author(s).published_or_final_versionSpringer Open Choice, 21 Feb 201

In search of knowledge: text mining dedicated to technical translation

Articolo pubblicato su CD e commercializzato direttamente dall'ASLIB (http://shop.emeraldinsight.com/product_info.htm/cPath/56_59/products_id/431). Programma del convegno su http://aslib.co.uk/conferences/tc_2011/programme.htm

Financiación y gobierno de la educación superior en el sudeste asiático

Peer Reviewe

The Research-Teaching Nexus: Not merely an enduring myth

For more than a century, whether or not the research-teaching nexus exists has remained an intensely debated issue in the global academy at both the conceptual and empirical levels. Situating teaching styles within the context of teaching, conceptualizing research agendas as a dimension of research, and using academic self-efficacy as a mediator, the present study empirically investigated the research-teaching nexus. Participants were 256 academics in science, technology, engineering, and mathematics (STEM) fields from all of the eight institutions funded by the University Grants Committee in Hong Kong. In the context of participating in the “Academic Profession in the Knowledge-based Society” (APIKS) international survey between late 2017 and early 2018, the participants responded to a short version of the Multi-Dimensional Research Agendas Inventory, a short version of the Research-Teaching Efficacy Inventory, and two scales from the Thinking Styles in Teaching Inventory. Results showed that academics’ research agendas statistically predicted their teaching styles – after age, gender, academic rank, and institutional ranking were considered. Furthermore,academic self-efficacy, especially research efficacy, provided a pathway from research agendas to one of the two teaching styles examined. Limitations and theoretical contributions of the research are discussed; and practical implications of the research findings are proposed for academics in STEM fields and for university senior managers.This research was funded by the General Research Fund (Grant number: 17604015) as administered by the University Grants Council of the Hong Kong Special Administrative Region, the People’s Republic of China

Neural networks for fatigue crack propagation predictions in real-time under uncertainty

Crack propagation analyses are fundamental for all mechanical structures for which safety must be guaranteed, e. g. as for the aviation and aerospace fields. The estimation of life for structures in presence of defects is a process inevitably affected by numerous and unavoidable uncertainty and variability sources, whose effects need to be quantified to avoid unexpected failures or excessive conservativism. In this work, residual fatigue life prediction models have been created through neural networks for the purpose of performing probabilistic life predictions of damaged structures in real-time and under stochastically varying input parameters. In detail, five different neural network architectures have been compared in terms of accuracy, computational runtimes and minimum number of samples needed for training, so to determine the ideal architecture with the strongest generalization power. The networks have been trained, validated and tested by using the fatigue life predictions computed by means of simulations developed with FEM and Monte Carlo methods. A real-world case study has been presented to show how the proposed approach can deliver accurate life predictions even when input data are uncertain and highly variable. Results demonstrated that the “H1-L1” neural network has been the best model, achieving an accuracy (Mean Square Error) of 4.8e-7 on the test dataset, and the best and the most stable results when decreasing the amount of data. Additionally, since requiring only very few parameters, its potential applicability for Structural Health Monitoring purposes in small cost-effective GPU devices resulted to be attractive

On the use of neural networks and statistical tools for nonlinear modeling and on-field diagnosis of solid oxide fuel cell stacks

Abstract The paper reports on the activities performed within the European funded project GENIUS to develop black-box models for modeling and diagnosis of solid oxide fuel cell (SOFC) stacks. Two modeling techniques were investigated, i.e. Neural Networks (NNs) and Statistical Tools (STs). The deployment of NNs was twofold: Recurrent Neural Networks (RNNs) and an NN classifier were developed to simulate transient operation of SOFCs and identify some specific faults that may occur in such devices, respectively. On the other hand, STs are based on a stepwise multiple regression. Data for model development were obtained from experiments specifically designed to reach maximal information content. The final aim was to obtain highly general models of SOFC stacks' operation in both transient and steady state. All the developed black-box models exhibited high accuracy and reliability on both training and test data-sets. Moreover, the black-box models were also proven effective in performing real-time monitoring and degradation analysis for different SOFC stack technologies