An organisational perspective on social exclusion in higher education : a case study

We explore organisational mechanisms underlying social exclusion in higher education, the latter defined as the underrepresentation of students from lower socio-economic backgrounds. We focus on “decoupling,†which is a central concept in organisational institutionalism referring to the construction of gaps between public commitment and core organisational practices, a common phenomenon in organisations worldwide. In the context of social inclusion this implies that universities are often publicly committed to social inclusion whereas their actual practices reproduce social exclusion. Drawing on an in-depth case study of a Flemish university, we identify four possible antecedents of decoupling: institutional contradictions resulting from the neo-liberalisation of higher education, uncertainty about effective inclusive practices, resistance of key constituencies and resource stringency resulting from experiences of lacking public funding

Branding of UK higher education institutions: an integrated perspective on the content and style of welcome adresses

The transformation to a more market-oriented steering approach in European higher education challenges universities and other higher education institutions to consider developing branding or image management activities. The existing literature focuses either on the content or the style, but we argue that an integrated perspective is needed to fully grasp the processes underlying branding. In a comparative case study of ten UK higher education institutions with varying reputations – five highly reputed versus five low(er) reputed institutions – we demonstrate how and why branding is deployed in welcome addresses of institutional leaders. Our findings indicate that isomorphic tendencies are visible, although brand differentiation could also be identified between highly and lowly reputed institutions. Our findings provide support for the competitive group perspective on branding activities

Monitoring and modelling of N2O emissions from innovative nitrogen removal processes

The emissions of greenhouse gases (such as N2O) from wastewater treatment is a matter of growing concern. The current atmospheric concentration of N2O, a potent greenhouse gas, is the highest in history. Conventional biological nitrogen removal is based on nitrification, i.e. conversion of ammonium to nitrate, followed by denitrification, i.e. conversion of nitrate to N2. Over the last 20 years, innovative nitrogen removal processes have been developed as an alternative, such as those based on the combined partial nitritation-anammox conversions which result in savings in aeration energy, no external carbon source, less CO2 emissions and sludge production. The overall goal of this PhD thesis was to elucidate the formation mechanisms of N2O from innovative nitrogen removal processes. To reach this goal, models were developed and applied in simulation studies. One of the first mechanistic models describing N2O formation by ammonia oxidizing bacteria was developed and formed the basis for later adaptations and extensions reflecting additional insights gathered. Monitoring campaigns were conducted on full-scale reactors for innovative nitrogen removal, including the development and application of a novel monitoring method and rigorous assessment of the gathered experimental data. The carbon footprint of the monitored full-scale partial nitritation reactor consisted almost entirely (92%) of N2O emissions. A novel method to measure dissolved N2O concentration on a minutely time scale was theoretically developed and applied on the full-scale reactor. The reactor off-gas N2O profile showed large variations during an operating cycle. This transient behaviour was exploited, enabling monitoring of the interphase transfer rate kLa and average N2O formation rates under different conditions, the latter was validated with the dissolved N2O measurements. By combining simulation and experimental results, it was found that the majority of N2O emissions was related to AOB, both under aerobic and anoxic conditions

Dynamic simulation of N2O emissions from a full-scale partial nitritation reactor

This study deals with the potential and the limitations of dynamic models for describing and predicting nitrous oxide (N2O) emissions associated with biological nitrogen removal from wastewater. The results of a three-week monitoring campaign on a full-scale partial nitritation reactor were reproduced through a state-of-the-art model including different biological N2O formation pathways. The partial nitritation reactor under study was a SHARON reactor treating the effluent from a municipal wastewater treatment plant sludge digester. A qualitative and quantitative comparison between experimental data and simulation results was performed to identify N2O formation pathways as well as for model identification. Heterotrophic denitrifying bacteria and ammonium oxidizing bacteria (AOB) were responsible for N2O formation under anoxic conditions, whereas under aerated conditions the AOB were the most important N2O producers. Relative to previously proposed models, hydroxylamine (NH2OH) had to be included as a state variable in the AOB conversions in order to describe potential N2O formation by AOB under anoxic conditions. An oxygen inhibition term in the corresponding reaction kinetics was required to fairly represent the relative contribution of the different AOB pathways for N2O production. Nevertheless, quantitative prediction of N2O emissions with models remains a challenge, which is discussed

How do university systems' features affect academic inbreeding? Career rules and language requirements in France, Germany, Italy and Spain

Author's accepted manuscript.Available from 19/01/2023.This is the peer reviewed version of the following article: Seeber, M. & Mampaey, J. (2021). How do university systems' features affect academic inbreeding? Career rules and language requirements in France, Germany, Italy and Spain. Higher Education Quarterly, which has been published in final form at https://doi.org/10.1111/hequ.12302. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Studies on academic inbreeding have mostly focused on institutional inbreeding and its negative effects, whereas little research has explored its causes. We identify current explanations of the macro‐, meso‐ and micro‐level factors that sustain academic inbreeding as well as research gaps. We address a main research gap regarding what macro‐level factors contribute to academic inbreeding, by analysing systems’ norms and rules regulating access to senior academic positions and teaching language requirements in France, Germany, Italy and Spain, the largest public university systems of the European Union. The analysis reveals that career rules designed to guarantee quality may have unintended effects in terms of academic inbreeding. Most importantly, the habilitation procedures pose greater challenges to international candidates and often increase barriers between disciplines as well. In some disciplines and regions, language requirements contribute substantially to academic inbreeding.acceptedVersio

Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS

The Sand Coated Die

The sand coated die is composed of a casting and a die which are separated by a layer of variable sand thickness. Increasing sand thickness will reduce the chilling influence of the die and hence augment the solidification time of the casting. A computer model has been developed which accurately predicts the relative solidification time in the sand coated die. This model, validated for several cast metals, is in close agreement with the experimental data of the present research as well as with the ones published previously in literature. At the interface sand-die no perfect conduction contact exists. This may be explained by a simplified model of sand grains packing