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This paper examines gender differences according to two new indicators in higher education studies. One indicator represents the length of studies beyond the minimum requirement and the other represents a harmonised graduation mark. We explore these indicators in a statistical framework that consists of Survival analysis Kaplan–Meier methods, Accelerated Failure Time models, Survival Trees, Multivariate Regression Trees and a modified Gender Parity Index. This unique combination of statistical methods allows both to analyse data involving censoring and to incorporate other explanatory variables in the analysis. The approaches were applied to data taken from a Greek and an Italian University and provide evidence that the survival analysis is a useful tool for exploring gender gaps in higher education while the Accelerated Failure Time model permits the investigation of how other variables can influence the gaps. The data mining techniques of survival trees and multivariate regression trees allows for the importance of such influencing variables to be measured and illustrates these in a simple to view tree structure. The Multivariate Regression Tree approach allows us to consider more than one continuous outcome variable so we were able to consider the two proposed indicators simultaneously. Interesting insights from this analysis are that gender has an important role and women outperform regarding these new indicators by taking less length of studies, less graduation time with higher performance, controlling also for other student characteristics. The modified gender parity index enriched the results in all stages.

Introduction Climate-related disasters have cost the world over £450 billion over the last 3 years. In the race to mitigate these effects, the UK government has committed to net-zero emissions by 2050. Transport provides the largest single sector contribution to CO2 emissions, the road network accounts for up to 91%. As the only UK country without a formal climate change bill Northern Ireland could compromise the overall effort. Case description In this research a survey of road asset owners, managers, academics, consultants, public transport providers was undertaken to seek to understand the current barriers to adapting a dispersed rural road network in Northern Ireland for net-zero transport. The survey data was collected though an online form with a combination of multiple choice and open ended questions. Thematic analysis was used to code and analyse the data collected which enabled a discussion around the key expert opinions gathered. Discussion and evaluation The paper presents details of the current road network in Northern Ireland and highlights some of the issues faced by asset owners. The survey questions were developed though engagement with transport professionals in Northern Ireland and focus predominantly on road use rather than the impact of current land management practices or environmental conditions such as flood risk. The response highlights a clear enthusiasm for change in the operation of the public road network which is hindered by a lack of government strategy and limited public consultation. Conclusions The high response rate (41%) for the survey highlights the interest of those in the transport sector to engage in activities which can support a better understanding of how road networks contribute to CO2 emissions. Within the survey data a requirement for behavioural change was highlighted as a key step to reduce transport related emissions, the enthusiasm for change demonstrates this is the optimum time to engage with the public and develop clear transport strategies. More accurate findings and empirical evidence could have been established had the study considered specific, transport planning, environmental and land use conditions for Northern Ireland. This will be the focus of further research in this area to enable clear translation of the research to other countries.

There is an enormous number of healthcare analytics applications in existence which have been embedded into healthcare systems with varying degrees of success. One of the key challenges is their need for access to sensitive patient data in a healthcare system that has a multitude of healthcare applications. This chapter introduces a new theoretical framework as an architecture in which Healthcare 4.0 applications can operate. The framework proposes using Apache Kafka as the core technology for creating data integration pipelines with the goal being to bring standardisation into the healthcare systems. The architecture offers a safe and secure environment in which multiple applications and algorithms from different organisations can seamlessly co-exist.

Increasing demand on hospital resources by an ageing population is impacting significantly on the number of beds available and, in turn, the length of time that elderly patients must wait for a bed before being admitted to hospital. This research presents a new methodology that models patient pathways and allows the accurate prediction of patient length of stay in hospital, using a phase-type survival tree to cluster patients based on their covariates and length of stay in hospital. A type of Markov model, called the conditional Coxian phase-type distribution is then implemented, with the probability density function for the time spent at a particular stage of care, for example, the first community discharge, conditioned on the length of stay experienced at the previous stage, namely the initial hospital admission. This component of the methodology is subsequently applied to each cohort of patients over a number of hospital and community stages in order to build up the profile of patient readmissions and associated timescales for each cohort. It is then possible to invert the methodology, so that the length of stay for an observation representing a new patient admission may be estimated at each stage of care, based on the assigned cohort at the initial hospital stage. This approach provides hospital managers with an accurate understanding of the rates with which different groups of patients move between hospital and community care, which may be used to reduce the negative effects of bed-blocking and the premature discharge of patients without a required period of convalescence. This has the benefit of assisting hospital managers with the effective allocation of vital healthcare resources. The approach presented is different to previous research in that it allows the inclusion of patient covariate information into the methodology describing patient transitions between hospital and community care stages in an aggregate Markov process. A data set containing hospital readmission data for elderly patients from the Abruzzo region of Italy is used as a case study in the application of the presented methodology.