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Many town centres in England exhibit high retail property vacancies and require regeneration. Several alternatives for the replacement of town centre retail (TCR) have been suggested, one of which is healthcare. The healthcare sector in England is in distress, with the National Health Service (NHS) tackling extensive patient waiting lists, whilst operating from an ageing estate. This paper is an introductory study that uses seven carefully selected personalised surveys to raise academic awareness of the importance and potential of integrating healthcare into town centres and calls for large-scale research to establish the statistical validity of the reported observations.

This study is developed from an interpretative standpoint. Through semi-structured interviews with key stakeholders specific to retail-to-healthcare conversions, this study reports stakeholders' perspectives on opportunities and limitations for such conversions to give direction for large statistical research in the future.

All participants support the integration of healthcare into town centres and agreed that diagnostic services, mental health support and primary care services are appropriate for provision within town centres. The participants advocate large-scale change in town centres in England, with integrated healthcare co-located with complementary services to fit with wider regeneration plans. Participants prefer adaptation of existing buildings where technically feasible and emphasise the importance of obtaining the buy-in of other stakeholders whilst expressing concerns about the uncertainty of capital funding availability.

This is the first study to analyse the practice of retail-to-healthcare conversions in town centres. These are still rare in England and projects are complex. The market experience is limited, and thus, the literature is scarce. This study fills this void and provides a starting point for future quantitative research in this area and informs the new town-planning policies.

The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

Using building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.

Digital images of the existing building conditions were captured using unmanned aerial vehicle (UAV), and were fed into Meshroom to generate the building’s geometry for 3D parametric model development. The model for the existing conventional building was created and converted to an energy model and exported to gbXML in Autodesk Revit for a whole building analysis which was carried out in the Green Building Studio (GBS). In the GBS, the conventional building was retrofitted into a green building to explore their energy consumption and CO2 emission.

By comparing the green building model to the conventional building model, the research found that the green building model saved 25% more energy while emitting 46.8% less CO2.

The study concluded that green building reduces energy consumption, thereby reducing the emission of CO2 into the environment. It is recommended that buildings should be simulated at the design stage to know their energy consumption and carbon emission performance before construction.

Occupant satisfaction, operation cost and environmental safety are essential for sustainable or green buildings. Green buildings increase the standard of living and enhance indoor air quality.

This investigation aided in a pool of information on how to use BIM methodology to retrofit existing conventional buildings into green buildings, showing how green buildings save the environment as compared to conventional buildings.

The aim of this paper is to discuss a selection of policy strategies, regional initiatives and market approaches to uncover the realities of twenty-first-century building energy performance. A position that market-based approaches, human influence and policy interventions are part of an ecosystem of building energy performance is presented.

An exploratory search of secondary sources spanning the last three decades was conducted. Both peer-reviewed and grey literature were included to capture a broader understanding of the discourse in literature. Research questions guided the literature search, and a data extraction tool was designed to categorise the literature. The primary limitation of this study is that only a few applications could be discussed in a condensed format.

Several challenges about the current status quo of building energy performance were identified and summarised as follows. (1) Inconsistencies in measurement and verification protocols, (2) Impacts of market approaches, (3) National policy priorities that are at variance with regional targets and (4) Ambiguous reporting on environmental impacts of energy efficiency (EE) technologies.

The practical implications of the findings in this paper for practice and research are that as part of the building energy performance ecosystem, national responses through government interventions must become adaptive to keep up with the fast-paced energy sector and social trends. Simultaneously, before market-based approaches overcome the messiness of socio-economic dynamics, institutional conditions and cultural nuances, they ought to transparently address environmental impacts and the infringement of several SDGs before they can become viable solutions to building energy performance.

This paper presents building energy performance as an ecosystem comprising human influence, market-based approaches and policy interventions which form interdependent parts of the whole. However, evidence in the literature shows that these aspects are usually investigated separately. By presenting them as an ecosystem, this paper contributes to the discourse by advocating the need to re-align building energy performance to socio-economic-political dynamics and contextually viable solutions.

The purpose of this paper is to report on the results of a study carried out to identify and analyse which potential subject areas may have impact on developments in the field of building maintenance (BM). That is, it is intended to contribute to the integration of new approaches so that building maintenance management (BMM) becomes as automated, digital and intelligent or smartness as possible in the near future.

The research approach has resulted in a theory that is essentially based on a qualitative design. The route followed was a literature review, involving the collection, analysis and interpretation of carefully selected information, mostly from recently published records. The data assembled and the empirical experience itself made it possible to present a comprehensive viewpoint and some future outlooks.

Five thematic areas considered as potentially impactful for BM developments have been highlighted, analysed and generically labelled as thematic base words, which are monitoring, automation, digitalisation, intelligence and smart. It is believed that these may be aspects that will lay the groundwork for a much more advanced and integrated agenda, featured by a high-tech vision.

This is thought to be a different way of looking at the problem, as it addresses five current issues together. Trendy technological aspects are quite innovative and advantageous for BMM, providing opportunities not yet widely explored and boosting the paradigm shift.

In recent years, cost overrun becomes a common problem in steel building construction projects. The average percentage can vary widely depending on the project type, size, complexity and location. The steel structure change ratio in Taiwan is from 1 to 18% in statistics. The contractors always put every possible effort into preventing or mitigating project cost overruns, and one of the approaches is an accurate cost overrun risk estimate. Traditional project cost overrun risk assessment models mainly focus on macro-level evaluation and may not function well for the project-specific level (micro-level). This study creates a network-like connection model between the outcome (i.e. cost overrun risk) and the associated root causes in which the project status evaluation checklists of design, manufacturing, construction and interfaces are used to evaluate the checklists' influences through the Bayesian network (BN) composed by intermediate causes.

Due to the constraint of data availability, BN nodes, relationships and conditional probabilities are defined to establish a BN-based steel building project cost overrun assessment model following the knowledge of experts. Because of the complexity of the BN, the construction of the BN structure is first to build BN's fault tree (FT) hierarchy. And then, basic BN framework is constructed by the transformation of the FT hierarchy. Furthermore, some worthwhile additional arcs among BN nodes are inserted if necessary. Furthermore, conditional probability tables (CPTs) among BN nodes are explored by experts following the concept of the ranked node. Finally, the BN-based model was validated against the final cost analysis reports of 15 steel building projects done in Taiwan and both were highly consistent. The overall BN-based model construction process consists of three steps: (1) FT construction and BN framework transformation, (2) CPT computation and (3) model validation.

This study established a network-like bridge model between the outcome (i.e. cost overrun risk) and the root causes in a network of which cost influences are evaluated through the project-specific status evaluation checklists of design, manufacturing, construction and interfaces. This study overcame several limitations of the previous cost overrun risk assessment models: (1) few past research support assessment of cost overrun based on real-time project-owned data and (2) the traditional causal models inadequately depict interdependencies among influence factors of cost overrun at the network. The main influence factors of the cost overrun risk at the steel building projects in Taiwan were also examined using sensitivity analysis. The main root causes of cost overrun in steel building projects are design management and interface integration.

The proposed model belongs to the project-specific causal assessment model using real-time project-owned status checklist data as input. Such a model was seldom surveyed in the past due to the complicated interdependence among causes in the network. For practical use, a convenient and simple regression equation was also developed to forecast the cost overrun risk of the steel building project based on the root causes as input. Based on the analysis of cost overrun risk and significant influence factors, proper tailor-made preventive strategies are established to reduce the occurrence of cost overrun at the project.

The Operations and Maintenance (O&M) cost of a facility is typically 60–85% of the total life cycle cost of a building whereas its design and construction cost accounts for only 5–10%. Therefore, enhancing and optimizing the O&M of a facility is a crucial issue. In addition, with the increasing complexities in a building's operating systems, more technologically advanced solutions are required for proactively maintaining a facility. Thereby, a tool is needed which can optimize and reduce the cost of facility maintenance. One of the solutions is Augmented or Mixed Reality (AR/MR) technologies which can reduce repair time, training time and streamline inspections. Therefore, the purpose of this study is to establish contextual knowledge of AR/MR application in facilities operation and maintenance and present an implementation framework through the analysis and classification of articles published between 2015 and 2022.

To effectively understand all AR/MR applications in facilities management (FM), a systematic literature review is performed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed for searching and describing the search strategies. Keywords were identified through the concept mapping technique. The Scopus database and Google Scholar were employed to find relevant articles, books and conference papers. A thorough bibliometric analysis was conducted using VOS Viewer and subsequently, a thematic analysis was performed for the selected publications.

The use of AR/MR within facilities O&M could be categorized into five different application areas: (1) visualization; (2) maintenance; (3) indoor localization and positioning; (4) information management and (5) indoor environment. After a thematic analysis of the literature, it was found that maintenance and indoor localization were the most frequently used research application domains. The chronological evolution of AR/MR in FM is also presented along with the origin of publications, which showed that the technology is out of its infancy stage and is ready for implementation. However, literature showed many challenges hindering this goal, that is (1) reluctance of the organizational leadership to bear the cost of hardware and trainings for the employees, (2) Lack of BIM use in FM and (3) system lagging, crashing and unable to register the real environment. A preliminary framework is presented to overcome these challenges.

This study accommodates a variety of application domains within facilities O&M. The publications were systematically selected from the existing literature and then reviewed to exhibit various AR/MR applications to support FM. There have been no literature reviews that focus on AR and/or MR in the FM and this paper fills the gap by not only presenting its applications but also developing an implementation framework.

This study analyses the socioeconomic impact of COVID-19 on government accountability regarding the employment of both national and migrant workforces by bringing evidence from an emerging market. In doing so, this study addresses if/how the government discharged its accountability to the public during this recent global health crisis, which started in late 2019, with its effects still being felt today.

This study is based on a close reading of the relevant news media (local and international), published research and official reports, as well as ten conversations with business managers to analyse the socioeconomic impact of COVID-19 on government accountability in the Kingdom of Saudi Arabia (KSA). This study draws on insights from public choice theory in trying to understand why some governments take an economic perspective while exercising accountability to their population during the pandemic.

It was found that COVID-19 led the government to pursue plans for the localization of the professions and increase employment rates among nationals vs. foreigners or migrant workers. The crisis was exploited by the government to achieve macro socio-political and economic goals, demonstrating its accountability to citizens, rather than foreign workers. This shift shows that difficult and exceptional circumstances can present opportunities for policymakers in emerging markets to achieve national policy and political aims.

This study enhances the author’s understanding of accountability during crises (i.e. crises-induced accountability) in emerging markets. The analyses presented enrich the crisis management literature by highlighting the implicit actions of national leaders that affect the lives and well-being of their constituents, especially vulnerable groups.

Built asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management.

Mixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review.

The research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA.

The contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually.

This study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.