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The purpose of this paper is to examine how cloud‐based information systems and services can support emerging and future requirements for sustainability governance of facilities.

The authors present basic elements of cloud‐based sustainability governance platforms, conduct a survey of existing industrial platforms and research works, discuss distinguishable and common characteristics of cloud computing platforms for sustainability governance, and give views on future research.

Cloud computing emerges as a potential candidate for supporting sustainability governance. However, several techniques must be provided in order to support multiple stakeholders, complex analysis and compliance processes.

The number of industrial platforms and research works in the survey is limited, as is information about industrial platforms. Furthermore, industrial platforms are continuously updated, thus some information might be outdated.

There exists no survey for understanding how cloud computing could be used for sustainability governance. The paper not only helps to understand state‐of‐the‐art in using cloud computing for sustainability governance but also discusses main components, stakeholders and requirements for cloud‐based sustainability governance platforms.

The misuse of pharmaceutical opioid analgesics is identified as a global public health concern. Codeine represents an interesting quandary in terms of its regulated status, with individuals varying in their metabolism of codeine, estimation of safe dosages, risk of adverse health consequences and abuse potential. Efforts to quantify and address hidden non-compliant medical codeine use, overuse and intentional misuse is compromised by availability to the public in prescribed and over the counter forms. The paper aims to discuss these issues.

A review of literature on codeine use, misuse and dependence, and associated innovative medical and pharmacy interventions is presented, and was conducted as part of a larger scoping review on codeine.

The review highlights the complexities associated with monitoring public health awareness of codeine's abuse potential, and customer/patients trends in non-compliant codeine use for therapeutic and recreational purposes. Aberrant codeine behaviours centre on visiting multiple doctors for prescriptions, repeated lost or stolen prescriptions, forging prescriptions and use of multiple pharmacies. Innovations to monitor misuse of codeine include national prescription databases and recent developments in real-time monitoring of dispensing activity.

Further development of real-time monitoring processes with process evaluation is advised.

This viewpoint is intended to demonstrate how efforts to quantify and address codeine use are compromised by its availability. It intends to encourage further policy and practitioner dialogue on how to monitor, support and intervene with consumers misusing codeine.

It is often important to acquire information such as temperature and strain values from metallic tools and structures in situ. With embedded sensors, structures are capable of monitoring parameters at critical locations not accessible to ordinary sensors. To embed sensors in the functional structures, especially metallic structures, layered manufacturing is a methodology capable of rapidly and economically integrating sensors during the production of tooling or structural components. Embedding techniques for both fiber‐optic sensors and thin‐film sensors have been developed.

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.

In the preceding three parts of this series, the authors have extensively reviewed and quantified the special processing sequences required for the ‘additive’ and ‘semi‐additive’ process strategies of PWB manufacture. In this, the fourth part of the series of five, they wish to present a series of full build processes which meet all the interconnect requirements of the 1990s while eliminating the drawbacks traditionally associated with additive processes.

The purpose of this paper is to review cotemporary maintenance programs and analyze factory production data for an SF₆ gas filled circuit breaker population. Various maintenance techniques and studies are reviewed to understand the reliability of circuit breaker models and the impact manufacturing can have on long term maintenance considerations.

Production and field event data were analyzed using statistical analysis tools. The population data were formatted so that a recurrent event analysis could be conducted to establish the mean cumulative function (MCF) by model and product family (class). Average Field Two‐year Recorded Event Rate (AFTRER) is introduced and compared to commonly used Field Incident Rate (FIR) and Mean‐Time between Failure (MTBF) measures.

Common managerial operating questions can be answered as exhibited for the provided circuit breaker population. This includes the longevity of field issues, the anticipated life cycle of a model or class, and AFTRER for models or classes of interest. These statistical analysis tools are used to make critical production quality and asset management observations and aid in decision‐making.

Due to limitations in existing database systems, the cost of events and explanatory variables related to event rates were not included in the analyses. There remains much work to be done in terms of the installation and retro‐fitting of breakers with conditions monitors in the field.

A framework to analyze maintenance data from fleet of similar assets using recurrent event data analysis is provided. The methods illustrated here would be useful for quality and asset managers to make operating decisions. This includes resource allocation decisions across a network of equipment.

Data analyzed are for power circuit breakers which are a critical element in the operation and reliability of the US power grid.

Using recurrent event data analysis to review and develop solutions to production quality and asset management problems including a comparison of AFTRER to FIR and MTBF measures.

Increasing complexity in construction projects evokes interest in application of innovative digital technologies in construction. Digital twins (DT), which bring these innovative technologies together, have strong interactions with lean construction (LC). To highlight the collaborative nature of DT and LC, the paper explores the interactions between LC and DT and assesses benefits, costs, opportunities and risks (BOCR) of DT in LC to analyze significant obstacles and enablers in DT adoption in LC.

BOCR approach comprehensively considers both the positive and the negative attributes of a problem. At the first step, BOCR criteria for DT are identified through literature review and expert opinions, at the second step dependencies among BOCR criteria for DT in LC are determined by neutrosophic analytic hierarchy process (AHP), through a questionnaire survey. Integrating BOCR into neutrosophic AHP enables achieving more meaningful preference scores.

Cost of skilled workforce is the most important factor and opportunity to reduce waste is the second most important factor in adoption of DT in LC. The results were analyzed to rank the BOCR of adoption of DT in LC.

This study, in a novel way, performs BOCR analysis through neutrosophic AHP to reflect experts' judgments more effectively by neutrosophic AHP's better handling of vagueness and uncertainty. The paper provides a model to better understand the significant factors that influence adoption of DT in LC.

Despite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.

The paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.

The paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.

The paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.

The efficiency of range telemetry, as currently practiced, is somewhat limited by the available display technology. The manpower, space and accuracy problems associated with current methods are examined, and the limitations of some new approaches are discussed The concept of Real Time Digital Strip Chart Emulation (RTDSCE), as exemplified by the ES2000 System, and the benefits inherent to such a technical approach are presented and discussed in detail These benefits include reduction of manpower and space requirements by the use of the workstation concept, the elimination of D/A conversion by direct recording of digitised signals, and the facilitation of critical realtime viewing through high resolution video monitoring.