esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 3.2.1 (r3613)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of four major components • esys.escript core library • finite element solver esys.finley (which uses fast vendor-supplied solvers or our paso linear solver library) • the meshing interface esys.pycad • a model library. The current version supports parallelization through both MPI for distributed memory and OpenMP for distributed shared memory. Please see Chapter 2 for changes to the way to launch esys.escript scripts. For more info on this and other changes from previous releases see Appendix B. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D

esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley. Release - 3.4.1 (r4596)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of four major components • esys.escript core library • finite element solver esys.finley (which uses fast vendor-supplied solvers or our paso linear solver library) • the meshing interface esys.pycad • a model library. The current version supports parallelization through both MPI for distributed memory and OpenMP for distributed shared memory. Please see Chapter 2 for changes to the way to launch esys.escript scripts. For more info on this and other changes from previous releases see Appendix B. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D

esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 3.4 (r4488)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of five major components • esys.escript core library • finite element solver esys.finley (which uses fast vendor-supplied solvers or our paso linear solver library) • the meshing interface esys.pycad • a model library. • an inversion library. The current version supports parallelization through both MPI for distributed memory and OpenMP for distributed shared memory. In this release there are a number of small changes which are not backwards compatible. Please see Appendix B to see if your scripts will be affected. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D. For Python3 support, see Appendix E

Ceding to their appetites: A taxonomy of international tourists to South Africa

Tourism is a key source of income to South Africa. Food and beverages is a key part of tourism and the literature reveals that tourists spend up to a quarter of their budget on cuisine. South Africa has, however, been rated as the least-prepared culinary travel destination and the travel destination with the greatest potential for growth. Therefore, a segmentation taxonomy based on culinary preferences of international tourists to South Africa is put forth which can be used to prepare South Africa as a culinary travel destination. The 627 international tourists surveyed were divided into five segments with the use of factor analyses, t-tests, Spearman rank correlations and analysis of variance. The segments were named conservationists, experience seekers, devotees, explorers and socialisers (CEDES taxonomy). Multiple results and implications are discussed in the paper

esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 3.4.2 (r4925)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of five major components • esys.escript core library • finite element solver esys.finley (which uses fast vendor-supplied solvers or our paso linear solver library) • the meshing interface esys.pycad • a model library. • an inversion library. The current version supports parallelization through both MPI for distributed memory and OpenMP for shared memory. In this release there are a number of small changes which are not backwards compatible. Please see Appendix B to see if your scripts will be affected. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D. For Python 3 support, see Appendix E

Drivers of success in implementing sustainable tourism policies in urban areas

The existing literature in the field of sustainable tourism highlights a number of barriers that impede the implementation of policies in this area. Yet, not many studies have so far considered the factors that would contribute to putting this concept into practice, and few address the case of urban areas. The concept of sustainability has only received limited attention in urban tourism research, even though large cities are recognised as one of the most important tourist destinations that attract vast numbers of visitors. Adopting a case study approach, this paper discusses a number of drivers of success identified by policy-makers in London to contribute to the implementation of sustainable tourisms policies at the local level, and briefly looks at the relationship between these drivers and the constraints perceived by the respondents to hinder the implementation of such policies in practice. These findings may help policy-makers in other large cities to successfully develop and implement policies towards sustainable development of tourism in their area

esys-Escript User’s Guide: Solving Partial Differential Equations with Escript and Finley Release - 4.0 (r5402)

esys.escript is a python-based environment for implementing mathematical models, in particular those based on coupled, non-linear, time-dependent partial differential equations. It consists of five major components: • esys.escript core library • finite element solvers esys.finley, esys.dudley, esys.ripley, and esys.speckley (which use fast vendor-supplied solvers or the included PASO linear solver library) • the meshing interface esys.pycad • a model library • an inversion module. All esys.escript modules should work under both python 2 and python 3, see Appendix E. The current version supports parallelization through MPI for distributed memory, OpenMP for shared memory on CPUs, as well as CUDA for some GPU-based solvers. This release comes with some significant changes and new features. Please see Appendix B for a detailed list. If you use this software in your research, then we would appreciate (but do not require) a citation. Some relevant references can be found in Appendix D

Impact of personality on educator attitudes towards open educational resources

The emergence of open educational resources (OER) represents one of the most significant educational developments in the 21st century. Given their capacity to be freely adapted, re-used, and shared in different contexts, OER expand the options for educators. This paper reports on an initial study concerning such choices in which educators’ personalities are investigated in relation to the OER adoption. Choosing open approaches has been shown to correlate with personal attitudes and several studies have highlighted the potential and need for investigating how personality might affect OER adoption. To address this gap, this study investigates the impact of educators’ personality differences in relation to OER adoption. The analysis focuses specifically on the perception towards OER and the intention to use OER using the Five-Factor Model (FFM) to identify educators’ personalities. Following a mixed methods approach, data collected from university educators using questionnaires (57 respondents) and interviews (15 respondents) are discussed in a two-stage hierarchical regression analysis. Demographic variables (age and gender) do not show any significant relationship. Findings reveal that while the explored five personality dimensions do not have an impact on the educator attitudes towards OER, they seem to have a significant impact on their intention to use OER. Specifically, only three personality dimensions – namely, extraversion, agreeableness, and openness – have a significant impact on the intention to use OER. This shows that ‘open attitude’ (mixing extraversion, and agreeableness, and openness) may be a fundamental prerequisite for educators to engage in open teaching practices, including the use of OER

An Exploratory Study of Value Added Services

Purpose: Using data from 104 countries over a six-year period (2009-2014), this study proposes a value-added predictor in service industries based on the eight indicators of the prosperity index, namely economy, entrepreneurship and opportunity, governance, education, health, safety and security, personal freedom, and social capital. Design/methodology/approach: The fuzzy-set qualitative comparative analysis (fsQCA) and complexity theory, a relatively novel approach for developing and testing the conceptual model, are used for asymmetric modelling of value added in service industries, and the predictive validity of the proposed configural model is tested. Findings: Apart from advancing method and theory, this study simulates causal conditions (i.e., recipes) leading to both high and low scores of the value added of services. The configural conditions indicating a high/low level of value added in service industries can be used as a guiding strategy for marketers, investors and policy makers. Originality/value: An analysis of worldwide data provides complex models demonstrating both how to regulate country conditions to achieve a high value-added score and select a foreign country for investment that offers a high level of value added service