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This paper presents a developed BIMxAR application, an integration of building information modeling (BIM) with augmented reality (AR) linked with real-time online database to support the building facility management work. The primary aim of this research was to develop and empirically examine the applicability of a BIM-based AR (BIMxAR) application in building facility management.

The BIMxAR application was developed and experimented with maintenance work of a university laboratory building. The experiment consisted of a comparison of supportive maintenance tasks performed using the traditional approach and the BIMxAR approach by 38 university students. The time taken to complete each task was recorded and analyzed using statistical analyses to compare the performance between the tasks completed using each approach.

The results indicated that the group using the BIMxAR application approach completed the tasks correctly in a significantly shorter time compared to that using the traditional approach. The findings supported the applicability of the developed BIMxAR application and the improvement of the building facility management tasks when using the proposed approach.

This paper presents a methodological approach in developing a mobile application that integrates BIM with AR for facility management work, leveraging real-time information exchange through a cloud-based platform. The paper also provides empirical evidence that demonstrates how the integration between BIM and AR could be achieved and implemented to help facilitate building maintenance tasks.

A smart city integrates a comprehensive suite of technologies, which inherently require data to function effectively. It is designed with the intention of amassing all available data concerning machines, devices, infrastructure, individuals and their surroundings. This commentary addresses the evolution of smart cities over time, the increasing extent of data collection, the growing pressure on personal privacy and people's reactions to these trends. The article highlights the contradiction between the needs of the city and the desires of its inhabitants. It notes that people react differently to gradual versus abrupt changes in data-collecting technologies and services. It also suggests that more work needs to be done to prepare both smart cities and the human population for a sustainable, mutually beneficial future.

This commentary presents a viewpoint on the subject of data collection and privacy in smart cities, drawing on various sources to support its observations and conclusions.

The primary focus of this discussion is on the technological evolution of the cities. It emphasizes that, as cities get smarter, they offer more conveniences in exchange for various types of data, highlighting the likelihood that pressure on personal privacy will continue to escalate. This is due to the increasing pervasiveness of data-collecting technologies in every aspect of lives and urban environments. These environments are expected to become progressively smarter each year. Given this context, and to ensure a seamless transition to smart and sustainable cities, it is imperative that today's privacy discussions start to focus not only just on the existing but also on the future conditions and challenges that citizens are expected to encounter.

This commentary delves into the existing gaps in understanding the contradiction between the data-collection “needs” of smart cities, the direction in which the cities are evolving and people's awareness of how much data they will have to surrender in the future. It also highlights the risk of people gradually relinquishing nearly all their privacy, often without noticing, in exchange for the ever-increasing conveniences offered by smart cities.

The COVID-19 pandemic has affected around 216 countries and territories worldwide and more than 2000 cities in India, alone. The smart cities mission (SCM) in India started in 2015 and 100 smart cities were selected to be initiated with a total project cost of INR 2031.72 billion. Smart city strategies play an important role in implementing the measures adopted by the government such as the issuance of social distancing regulations and other COVID-19 mitigation strategies. However, there is no research reported on the role of smart cities strategies in managing the COVID-19 outbreak in developing countries.

This paper aims to address the research gap in smart cities, technology and healthcare management through a review of the literature and primary data collected using semi-structured interviews.

Each city is unique and has different challenges, the study revealed six key findings on how smart cities in India managed the COVID-19 outbreak. They used: Integrated Command and Control Centres, Artificial Intelligence and Innovative Application-based Solutions, Smart Waste Management Solutions, Smart Healthcare Management, Smart Data Management and Smart Surveillance.

This paper contributes to informing policymakers of key lessons learnt from the management of COVID-19 in developing countries like India from a smart cities’ perspective. This paper draws on the six Cs for the implications directed to leaders and decision-makers to rethink and act on COVID-19. The six Cs are: Crisis management leadership, Credible communication, Collaboration, Creative governance, Capturing knowledge and Capacity building.

This study aims to examine the perceptions and evaluations of Muslim COVID-19 survivors and health workers regarding the halal, business and ethical attributes of hospitals during their interactions related to COVID-19 treatment.

Descriptive qualitative research with semi-structured online interviews was used to gather insights from COVID-19 survivors and health workers who treated COVID-19 patients. The findings were then compared with existing literature on hospital services and Sharia attributes.

The study found that patients and health-care workers in hospitals are concerned about whether the hospital follows Sharia law, the quality of health-care and hospital services and the ethical conduct of hospital staff. This is especially true during the COVID-19 pandemic, when patients are more anxious about religious conduct and the afterlife.

Hospitals need to address halal attributes in all aspects of their services for Muslim patients and business attributes such as standard health-care quality, service quality and ethical attributes. Participants indicated that when these needs are met, they are more likely to revisit the hospital and recommend it to others.

This study contributes to understanding the expectations of Muslim patients regarding hospital services that meet Islamic ethical and business requirements. Using the COVID-19 pandemic as a case study broadens the understanding of how to better serve Muslim customers.

The purpose of this study is to investigate the usage of digital technologies (DTs) in improving the mental health of workers on construction sites.

A mixed research approach was used in the study, which comprised a questionnaire survey and two phases of semi-structured interviews. Purposive sampling was used to determine the interviewees and respondents of the questionnaire survey. Weighted mean rating (WMR) and manual content analysis were used to rank and evaluate the collected data.

The findings of this study revealed bipolar disorder, anxiety disorders, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, work-related stress and depression as the six most significant mental disorders (MDs) among the construction workforce and 30 causes for them. Moreover, 27 symptoms were related to the six most significant MDs, and sweating was the most significant symptom among them. Despite that, 16 DTs were found to be suitable in mitigating the causes for the most significant MDs.

There are numerous studies conducted on the application of DTs to construction operations. However, insufficient studies have been conducted focusing on the application of DTs in improving the mental health of workers at construction sites. This study can thus influence the use of DTs for tackling the common causes for MDs by bringing a new paradigm to the construction industry.

Initiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.

We compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.

By analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.

Our findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.

This study aims to explore the innovation deployment of construction contracting firms through exploratory–exploitative learning and organisational ambidexterity.

Firstly, a literature-based conceptual framework was developed to explain innovation implementation through exploratory–exploitative learning and organisational ambidexterity. A prominent Hong Kong construction contracting firm was then selected as the case study to explore its innovation deployment at different organisational levels (i.e. firm and project levels). Qualitative data were attained by conducting 12 semi-structured interviews with industry experts and document analysis. The thematic analysis using NVivo 12 software was adopted to analyse data.

Findings reveal that the case study firm successfully fosters innovation when ambidexterity is achieved through the balance between exploratory (i.e. radical innovation) and exploitative learnings (i.e. incremental innovation).

Establishing uniform ambidexterity (i.e. 50:50) at the firm or project level is not mandatory to deploy innovation successfully. The ratio can vary based on the characteristics and requirements of construction firms.

This paper shall motivate construction practitioners to adopt radical–incremental innovation ambidexterity in firms and ultimately enhance the productivity and efficiency of the construction industry.

Previous construction innovation research has frequently explored firm or project-level innovation separately. This study identified a multi-level focus on innovation. Through the lens of exploratory–exploitative theory, different forms of innovation ambidexterity for different levels are suggested rather than one specific ambidexterity.

This study examines academic librarians' perceptions and attitudes toward Internet of Things (IoT) applications in Hong Kong academic libraries and the problems and possible improvements in using IoT technologies to strengthen library services.

This qualitative research used video conferencing software for semi-structured, one-on-one interviews. Participants were given introductory material about the IoT and asked to complete an interview. The data were analyzed using inductive theme clustering for this study.

The analysis identified three themes: perception about applying IoT technology to the library, problems and improvements in using IoT. Participants were generally optimistic about the potential benefits of IoT for improving library operations and providing personalized services. However, they also expressed concerns about privacy and security, errors and extra efforts for information literacy training. They suggested improvements such as incorporating facial recognition technology, advanced RFID technology and collections identification technology to enhance user experience.

Most studies examined users' views rather than librarians' on IoT applications, which few studies cover, especially in East Asia.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

Though daycare centres are built to facilitate children's social and physical growth, divergent stakeholder perceptions yield conflicts while managing risks for each space due to the minimal guidelines in the Republic of Korea. Due to the unclearly defined knowledge and experience gap between representative stakeholders, teachers have difficulties in operating it with high satisfaction. This study therefore was conducted to systematically identify which parts of performance should be increased to preferentially increase end-user satisfaction in limited resources.

This research conducted quantitative analysis gradually. Importance–performance analysis (IPA) grasped the perceptions of construction practitioners and end-users after identifying space composition and representative risks of daycare centres. Based on the factors influencing perception differences, principal component analysis and structural equation modelling (SEM) were conducted to determine which space improves the performance preferentially to increase overall end-user satisfaction.

As the utilisation of indoor spaces increases after COVID-19, improvement of indoor spaces except for indoor playroom is necessary, especially, advancement of the quality of teachers' rooms.

Outputs by reflecting the end-user experiences support to understand which spaces are managed from the limited resources.

Theoretical framework for daycare centre was established in Korea where guidelines were insufficient, and differences in perception of each risk of stakeholders were identified. Moreover, gradual statistical analysis was set to construct end-user-focused centres by extracting where the performance of space should be improved in prior.