Case Study: Enhancing the Learning Experiences of BAME Students at a University: The University Role

This case study examines the role of a university and academics in improving the learning experiences of BAME students, drawing on student-led participatory action research with Social Sciences BAME students at Bournemouth University (BU henceforth) between 2018-2020. The paper seeks to illuminate the critical role of the university by focusing on three inter-related facets at macro, meso and micro levels (Bronfenbrenner, 1979): financial and temporal/spatial support for students; collaboration between academic staff at departmental and faculty levels to address any issues that arose from student meetings; and its consequential impact on student wellbeing, self-worth and their overall engagement in their learning. I argue that to achieve the utmost improvement in BAME students’ learning experiences, these different levels of the support system need to work together. I further argue that maximising the potentiality of ‘ethnic capital’ (Modood, 2004) could be a powerful resource that could bring significant changes to the experiences of BAME students and subsequent outcomes of their learning during and after university.

Chiller Load Forecasting Using Hyper-Gaussian Nets

Energy load forecasting for optimization of chiller operation is a topic that has been receiving increasing attention in recent years. From an engineering perspective, the methodology for designing and deploying a forecasting system for chiller operation should take into account several issues regarding prediction horizon, available data, selection of variables, model selection and adaptation. In this paper these issues are parsed to develop a neural forecaster. The method combines previous ideas such as basis expansions and local models. In particular, hyper-gaussians are proposed to provide spatial support (in input space) to models that can use auto-regressive, exogenous and past errors as variables, constituting thus a particular case of NARMAX modelling. Tests using real data from different world locations are given showing the expected performance of the proposal with respect to the objectives and allowing a comparison with other approaches.

MODIFIED EQUATION FOR A CLASS OF EXPLICIT AND IMPLICIT SCHEMES SOLVING ONE-DIMENSIONAL ADVECTION PROBLEM

This paper presents the general modified equation for a family of finite-difference schemes solving one-dimensional advection equation. The whole family of explicit and implicit schemes working at two time-levels and having three point spatial support is considered. Some of the classical schemes (upwind, Lax-Friedrichs, Lax-Wendroff) are discussed as examples, showing the possible implications arising from the modified equation to the properties of the considered numerical methods.

Crossing the left-right party divide? Understanding the electoral success of the Czech Pirate Party in the 2017 parliamentary elections

In the 2017 Czech parliamentary election, the Czech Pirate Party (Pirates) gained 10.79% of the votes – an unprecedented success, compared to most of the pirate parties across Europe. However, as their electoral gain varies widely across the Czech Republic’s territory, this article analyses all (more than 6000) Czech municipalities in the elections of 2010, 2013, and 2017 to explain this variation. Overall, the success of the Pirates was driven especially by obtaining much more support in larger municipalities with younger populations (although not only those aged 18–24 but also older ones), lower unemployment, higher turnout, and lower support for leftist parties. Thus, from a spatial perspective, the patterns of Pirate voting largely resembled long-term spatial support for Czech rightist parties and we can conclude that the Pirates made considerable inroads to regions which had historically been strongholds of the Civic Democratic Party, as the former main party of the right, but also strongholds of minor right-wing (‘liberal centre’) parties of the 1990s and early 2000s. Success of the Pirates thus was based especially on votes from municipalities located in more developed areas, where the Pirates received many more votes than in structurally disadvantaged regions.

Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m, and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programs by providing reliable parameter estimates at the same spatial support, and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m, and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programs by providing reliable parameter estimates at the same spatial support, and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns. Keywords: schistosomiasis modelling; modifiable areal unit problem; uncertainty; Bayesian statistics; convolution model.