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This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients.

A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification.

The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection.

This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

Nowadays, there are strong movements towards development and usage of multimedia courseware as a means of knowledge transfer. Many authors of textbooks or lecture notes are now striving to redesign the supporting material for their major courses in a structured, highly efficient way, including interactive content and media. Thus, in order to avoid unnecessary work load resulting from updating and publishing various courseware versions, tools for improving document creation and conversion have been developed and are now being applied for the first time on a new “Electrodynamics”‐‐ courseware.

The purpose of this paper is to carry out an analytical approximation model (heat transfer model (HTM)) for the calculation of the heat transfer coefficient at the end winding bars of large hydro generators. These coefficients are needed for lumped parameter thermal models in the design process.

The computational fluid dynamics simulation in combination with conjugate heat transfer (CHT) validates the accuracy of the HTM. The theoretical approach describes the formulation of the heat transfer coefficient and the Gauss-Newton method has been applied to find the coefficients of the approximation model.

The paper describes the new analytical approximation model for the heat transfer coefficient at the end winding bars of hydro generators and shows also the validation to simulation results.

The analytical approximation model for the heat transfer coefficient at the end winding bars has been described and a comparison with CHT results has shown a good agreement.

There is an urgent need to translate climate change awareness into tangible climate adaptation strategies. The built environment is identified as one of the kick-off points in making climate change adaptation as the built environment shares a dual-way relationship. While the built environment largely contributes to the climate change-triggering factors, it also becomes highly vulnerable in the face of climate change impacts. Tied up with the interconnectedness of the built environment processes and associated systems, the involvement of numerous stakeholders from different spectrums creates the need for a holistic and multi-stakeholder approach in developing climate response strategies for the built environment. Accordingly, this study aims to identify the roles and responsibilities of the different built environment stakeholders in climate change adaptation.

The study consisted of a scoping review at the initial stage, contextualising studies based on secondary data, and semi-structured expert interviews in five different countries: the UK, Sweden, Malta, Spain and Sri Lanka. The paper summarises the findings of the individual country-level desk studies and 65 built environment stakeholder interviews representing national and local governments, communities, academia and research organisations, civil organisations, professional bodies and the private sector. The findings were validated through focus group discussions in two stakeholder seminars.

The findings summarised a set of key roles and sub-roles for each stakeholder category, considering the current status and needs. The national governments need to set a long-term vision, enabling multi-sector interventions while promoting investment and innovation in climate change adaptation. The local governments overlook local adaptation plans, while the community is responsible for decarbonising operations and practising adaptation at the local level. Civil organisations and professional bodies are the voice of the community, linking policy and practice. Academia and research are responsible for nurturing skills and new knowledge, and the private sector must contribute by adopting climate resilience into their business portfolio and corporate social responsibility.

This research is part of an Europe-Union-funded research project, Built Environment leArning for Climate Adaptation (BEACON), which aims to develop skills and competencies of the built environment professionals so that they will be adequately equipped to handle the adaptation process of the built environment needs to adapt in facing the climate change impacts.

The paper provides an in-depth analysis of the roles and responsibilities pertaining to each category of the different stakeholders in effectively adapting the built environment to withstand the climate change consequences. Demarcation of each stakeholder’s roles and responsibilities separately facilitates collaboration and coordination between the different parties and provides a more holistic approach to climate change adaptation in the built environment.

This paper aims to compare the automatic segmentation of medical data and conversion to stereolithography (STL) skull models using open-source software versus commercial software.

Both open-source and commercial software used automatic segmentation and post-processing of the data without user intervention, thus avoiding human error. Detailed steps were provided for comparisons and easier to be repeated by other researchers. The results of segmentation, which were converted to STL format were compared using geometric analysis.

STL skull models produced using open-source software are comparable with the one produced using commercial software. A comparison of STL skull model produced using InVesalius with STL skull model produced using MIMICS resulted in an average dice similarity coefficient (DSC) of 97.6 ± 0.04 per cent and Hausdorff distance (HD) of 0.01 ± 0.005 mm. Inter-rater study for repeatability on MIMICS software yielded an average DSC of 100 per cent and HD of 0.

The application of open-source software will benefit the small research institutions or hospitals to produce and virtualise three-dimensional model of the skulls for teaching or clinical purposes without having to purchase expensive commercial software. It is also easily reproduceable by other researchers.

This study is one of the first comparative evaluations of an open-source software with propriety commercial software in producing accurate STL skull models. Inaccurate STL models can lead to inaccurate pre-operative planning or unfit implant.

The paper aims to compare and discuss the findings of discursive constructions of patients in legal texts from the three Scandinavian countries. Since traditional welfare state systems in Scandinavia are being challenged by new governance systems, new questions are being raised about patient positions and agency, carrying with them potential ethical dilemmas for healthcare professionals.

The methodology of the paper is inspired by critical discourse analysis. Comprehensively analysing the findings of previous discourse studies on how “the patient” is constructed in central policy texts, this study compares the position of the patient in Norway, Sweden and Denmark.

The paper reveals ideological struggles across the Scandinavian countries, operating at a political level, a legislative level and a healthcare level. It is shown that national governance systems still exert hegemonic power by strongly influencing patients' degree of choice and autonomy. The discursive struggle between welfare state governance and other governance systems in Scandinavia indicates a shift towards a commercial healthcare market although a traditional welfare model is advocated by professionals and researchers.

Because of the specific conditions of Scandinavian healthcare policy, the findings lack generalisability. The research approach should therefore be explored further in additional contexts.

The findings of this study can inform policymakers, professionals and patients of the ideological values underlying seemingly objective shifts in national policy.

A comparative critical discourse analysis can expose patterns in the Scandinavian approaches to patient rights.

This paper aims to determine whether prosocial motivation acts as a mediator between the individual motivation types of self-determination theory and knowledge sharing.

A partial least squares structural equation model (PLS-SEM) based on data collection (N = 303) ) was calculated, using “Smart PLS 3” software.

In respect of the influence of individual motivation types on knowledge sharing, it was found that prosocial motivation provides indirect mediation for external motivation and complementary mediation for introjected motivation, whereas it has no mediation effect on intrinsic and identified motivation.

Future research should consider the use of prosocial motivation as a mediator to reveal indirect effects that, otherwise, are at risk of remaining hidden.

To foster knowledge sharing within organizations, measures should be taken to increase external, introjected and prosocial motivation, as it was found that these types of motivation have a significant positive impact on knowledge sharing.

The current unanswered question – whether or not prosocial motivation acts as a mediator between the motivation types of self-determination theory and knowledge sharing – is examined, thereby providing insights into the hitherto largely unexplored role of prosocial motivation in knowledge-sharing models.

Describes part of a wider evaluation exercise undertaken to assess the impact of the introduction of a picture‐archiving and communication system (PACS) on the adult intensive care unit (AICU) at the Royal Brompton NHS Trust in London. The objectives of this research were to evaluate the perceptions of PACS of the medical and ancillary staff working within AICU as well as to undertake a preliminary assessment of its impact on the workload of radiographers. Questionnaires, interviews and a process analysis were undertaken. The research findings indicate that the overall perception of staff towards the introduction of the PACS was positive. The impact of the system on the workload of radiographers was significant, reducing the time taken to obtain an image from 90 to 60 minutes. However, lessons to be learned for future PACS implementations include the need to ensure compatibility with existing IT systems, adequate IT support and initiatives to ensure that the benefits of PACS are communicated to the hospital at large.

The purpose of this paper is to develop surrogate models, using deep learning (DL), that can facilitate the application of EM analysis software. In the current status quo, electrical systems can be found in an ever-increasing range of products that are part of everyone’s daily live. With the advances in technology, industries such as the automotive, communications and medical devices have been disrupted with new electrical and electronic systems. The innovation and development of such systems with increasing complexity over time has been supported by the increased use of electromagnetic (EM) analysis software. Such software enables engineers to virtually design, analyze and optimize EM systems without the need for building physical prototypes, thus helping to shorten the development cycles and consequently cut costs.

The industry standard for simulating EM problems is using either the finite difference method or the finite element method (FEM). Optimization of the design process using such methods requires significant computational resources and time. With the emergence of artificial intelligence, along with specialized tools for automatic differentiation, the use of DL has become computationally much more efficient and cheaper. These advances in machine learning have ushered in a new era in EM simulations where engineers can compute results much faster while maintaining a certain level of accuracy.

This paper proposed two different models that can compute the magnetic field distribution in EM systems. The first model is based on a recurrent neural network, which is trained through a data-driven supervised learning method. The second model is an extension to the first with the incorporation of additional physics-based information to the authors’ model. Such a DL model, which is constrained by the laws of physics, is known as a physics-informed neural network. The solutions when compared with the ground truth, computed using FEM, show promising accuracy for the authors’ DL models while reducing the computation time and resources required, as compared to previous implementations in the literature.

The paper proposes a neural network architecture and is trained with two different learning methodologies, namely, supervised and physics-based. The working of the network along with the different learning methodologies is validated over several EM problems with varying levels of complexity. Furthermore, a comparative study is performed regarding performance accuracy and computational cost to establish the efficacy of different architectures and learning methodologies.

The purpose of this paper is to investigate the applicability of a direct current (DC) hysteresis measurement on power transformer terminals for the subsequent hysteresis model parametrization in transformer grey box topology models.

Two transformer topology models with two different hysteresis models are used together with a DC hysteresis measurement via the power transformer terminals to parameterize the hysteresis models by means of an optimization. The calculated current waveform with the derived model in the transformer no-load condition is compared to the measured no-load current waveforms to validate the model.

The proposed DC hysteresis measurement via the power transformer terminals is suitable to parametrize two hysteresis models implemented in transformer topology models to calculate the no-load current waveforms.

Different approaches for the measurement and utilization of transformer terminal measurements for the hysteresis model parametrization are discussed in literature. The transformer topology models, derived with the presented approach, are able to reproduce the transformer no-load current waveform with acceptable accuracy.