The rapid proliferation of internet-connected devices in public and private spaces offers humanity numerous conveniences, including many safety benefits. However, unlocking the full potential of the Internet of Things (IoT) would require the assurance that IoT devices and applications do not pose any safety hazards to the stakeholders. While numerous efforts have been made to address security-related challenges in the IoT environment, safety issues have yet to receive similar attention. The safety attribute of IoT systems has been one of the system’s vital non-functional properties and a remarkable attribute of its dependability. IoT systems are susceptible to safety breaches due to a variety of factors, such as hardware failures, misconfigurations, conflicting interactions of devices, human error, and deliberate attacks. Maintaining safety requirements is challenging due to the complexity, autonomy, and heterogeneity of the IoT environment. This article explores safety challenges across the IoT architecture and some application domains and highlights the importance of safety attributes, requirements, and mechanisms in IoT design. By analysing these issues, we can protect people from hazards that could negatively impact their health, safety, and the environment.

Fuzzy data-driven reliability analysis has been used in different safety-critical domains for risk assessment and decision-making where precise failure data is non-existent. Expert judgements and fuzzy set theory have been combined with different variants of fault trees as part of fuzzy data-driven reliability analysis studies. In such fuzzy fault tree analyses, different people represented failure data using different membership functions for the fuzzy set, and different parameters were set differently in the expert opinion elicitation process. Due to the availability of a wide variety of options, it is possible to obtain different outcomes when choosing one option over another. This article performed an analysis in the context of fuzzy data-based temporal fault tree analysis to investigate the effect of choosing different membership functions on the estimated system reliability and criticality ranking of different failure events. Moreover, the effect of using different values for the relaxation factor, a parameter set during the expert elicitation process, was studied on the system reliability and criticality evaluation. The experiments on the fuel distribution system case study show system reliability did not vary when triangular and trapezoidal fuzzy numbers were used with the same upper and lower bounds. However, it was seen that the criticality rankings of a couple of events were changed due to choosing different membership functions and different values of relaxation factor

Big data application is a significant transformative driver of change in the retail, health, engineering, and advanced manufacturing sectors. Big data studies in construction are still somewhat limited, although there is increasing interest in what big data application could achieve. Through interviews with construction professionals, this paper identifies the capabilities needed in construction firms to enable the accrual of the potentially transformative benefits of big data application in construction. Based on previous studies, big data application capabilities, needed to transform construction processes, focussed on data, people, technology, and organisation. However, the findings of this research suggest a critical modification to that focus to include knowledge and the organisational environment along with people, data, and technology. The research findings show that construction firms use big data with a combination strategy to enable transformation by (a) driving an in-house data management policy to rolling-out the big data capabilities; (b) fostering collaborative capabilities with external firms for resource development, and (c) outsourcing big data services to address the capabilities deficits impacting digital transformation.

The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

Process safety has drawn increasing attention in recent years and has been investigated from different perspectives, such as quantitative risk analysis, consequence modeling, and regulations. However, rare attempts have been made to focus on inherent safety design assessment, despite being the most cost-effective safety tactic and its vital role in sustainable development and safe operation of process infrastructure. Accordingly, the present research proposed a knowledge-driven model to assess inherent safety in process infrastructure under uncertainty. We first developed a holistic taxonomy of contributing factors into inherent safety design considering chemical, reaction, process, equipment, human factors, and organizational concerns associated with process plants. Then, we used subject matter experts, content validity ratio (CVR), and content validity index (CVI) to validate the taxonomy and data collection tools. We then employed a fuzzy inference system and the Extent Analysis (EA) method for knowledge acquisition under uncertainty. We tested the proposed model on a steam methane-reforming plant that produces hydrogen as renewable energy. The findings revealed the most contributing factors and indicators to improve the inherent safety design in the studied plant and effectively support the decision-making process to assign proper safety countermeasures.

Underwater communication applications extensively use localization services for object identification. Because of their significant impact on ocean exploration and monitoring, underwater wireless sensor networks (UWSN) are becoming increasingly popular, and acoustic communications have largely overtaken radio frequency (RF) broadcasts as the dominant means of communication. The two localization methods that are most frequently employed are those that estimate the angle of arrival (AOA) and the time difference of arrival (TDoA). The military and civilian sectors rely heavily on UWSN for object identification in the underwater environment. As a result, there is a need in UWSN for an accurate localization technique that accounts for dynamic nature of the underwater environment. Time and position data are the two key parameters to accurately define the position of an object. Moreover, due to climate change there is now a need to constrain energy consumption by UWSN to limit carbon emission to meet net-zero target by 2050. To meet these challenges, we have developed an efficient localization algorithm for determining an object position based on the angle and distance of arrival of beacon signals. We have considered the factors like sensor nodes not being in time sync with each other and the fact that the speed of sound varies in water. Our simulation results show that the proposed approach can achieve great localization accuracy while accounting for temporal synchronization inaccuracies. When compared to existing localization approaches, the mean estimation error (MEE) and energy consumption figures, the proposed approach outperforms them. The MEEs is shown to vary between 84.2154m and 93.8275m for four trials, 61.2256m and 92.7956m for eight trials, and 42.6584m and 119.5228m for twelve trials. Comparatively, the distance-based measurements show higher accuracy than the angle-based measurements.

Video capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of gastric disorders. However, owing to the high redundancy and subtle manifestation of anomalies among thousands of frames, the manual construal of VCE videos requires considerable patience, focus, and time. The automatic analysis of these videos using computational methods is a challenge as the capsule is untamed in motion and captures frames inaptly. Several machine learning (ML) methods, including recent deep convolutional neural networks approaches, have been adopted after evaluating their potential of improving the VCE analysis. However, the clinical impact of these methods is yet to be investigated. This survey aimed to highlight the gaps between existing ML-based research methodologies and clinically significant rules recently established by gastroenterologists based on VCE. A framework for interpreting raw frames into contextually relevant frame-level findings and subsequently merging these findings with meta-data to obtain a disease-level diagnosis was formulated. Frame-level findings can be more intelligible for discriminative learning when organized in a taxonomical hierarchy. The proposed taxonomical hierarchy, which is formulated based on pathological and visual similarities, may yield better classification metrics by setting inference classes at a higher level than training classes. Mapping from the frame level to the disease level was structured in the form of a graph based on clinical relevance inspired by the recent international consensus developed by domain experts. Furthermore, existing methods for VCE summarization, classification, segmentation, detection, and localization were critically evaluated and compared based on aspects deemed significant by clinicians. Numerous studies pertain to single anomaly detection instead of a pragmatic approach in a clinical setting. The challenges and opportunities associated with VCE analysis were delineated. A focus on maximizing the discriminative power of features corresponding to various subtle lesions and anomalies may help cope with the diverse and mimicking nature of different VCE frames. Large multicenter datasets must be created to cope with data sparsity, bias, and class imbalance. Explainability, reliability, traceability, and transparency are important for an ML-based diagnostics system in a VCE. Existing ethical and legal bindings narrow the scope of possibilities where ML can potentially be leveraged in healthcare. Despite these limitations, ML based video capsule endoscopy will revolutionize clinical practice, aiding clinicians in rapid and accurate diagnosis.

In this work, we considered model-based simulation and optimisation of a medium scale brackish water desalination process. The mathematical model is validated using actual multistage RO plant data of Al- Hashemite University (Jordan). Using the validated model, the sensitivity of different operating parameters such as pump pressure, brackish water flow rate and seasonal water temperature (covering the whole year) on the performance indicators such as productivity, product salinity and specific energy consumption of the process is conducted. For a given feed flow rate and pump pressure, winter season produces less freshwater that in summer in line with the assumption that winter water demand is less than that in summer. With the soaring energy prices globally, any opportunity for the reduction of energy is not only desirable from the economic point of view but is an absolute necessity to meet the net zero carbon emission pledge by many nations, as globally most desalination plants use fossil fuel as the main source of energy. Therefore, the second part of this paper attempts to minimise the specific energy consumption of the RO system using model-based optimisation technique. The study resulted not only 19 % reduction in specific energy but also 4.46 % increase in productivity in a particular season of the year. For fixed product demand, this opens the opportunity for scheduling cleaning and maintenance of the RO process without having to consider full system shutdown.

Multistage Flash (MSF) desalination process is still a dominant process, especially in the Gulf region, to produce high quality freshwater. Although there has been energy price surge in recent years, MSF process will continue to operate in that region for some foreseeable future. The key challenge is how to make such processes still profitable. Understanding the dynamics of any processes under uncertainty and disturbances is very important to make a process operationally feasible and profitable. The main aim of this work is to understand the dynamics of industrial scale MSF process using high fidelity and reliable process model. For this purpose, a detailed dynamic model for the MSF process incorporating key and new features is developed and validated against the actual data of a large-scale seawater desalination plant. The model is then used to study the behaviour of large scale MSF processes for disturbances in steam temperature, feed temperature and the recycle brine flow rate. The simulation results show that the last stage requires a longer time to settle compared to the preceding stages. In addition, steam temperature shows insignificant influence on the performance ratio compared to the inlet seawater temperature and recycle brine flow rate. Furthermore, it is found that the productivity of plant can increase in the winter compared to that in the summer. However, this benefit comes at the expense of increased steam consumption in the winter, resulting in a low performance ratio.

Background: National audits aim to reduce variations in quality by stimulating quality improvement. However, varying provider engagement with audit data means that this is not being realised. Aim: The aim of the study was to develop and evaluate a quality dashboard (i.e. QualDash) to support clinical teams’ and managers’ use of national audit data. Design: The study was a realist evaluation and biography of artefacts study. Setting: The study involved five NHS acute trusts. Methods and results: In phase 1, we developed a theory of national audits through interviews. Data use was supported by data access, audit staff skilled to produce data visualisations, data timeliness and quality, and the importance of perceived metrics. Data were mainly used by clinical teams. Organisational-level staff questioned the legitimacy of national audits. In phase 2, QualDash was co-designed and the QualDash theory was developed. QualDash provides interactive customisable visualisations to enable the exploration of relationships between variables. Locating QualDash on site servers gave users control of data upload frequency. In phase 3, we developed an adoption strategy through focus groups. ‘Champions’, awareness-raising through e-bulletins and demonstrations, and quick reference tools were agreed. In phase 4, we tested the QualDash theory using a mixed-methods evaluation. Constraints on use were metric configurations that did not match users’ expectations, affecting champions’ willingness to promote QualDash, and limited computing resources. Easy customisability supported use. The greatest use was where data use was previously constrained. In these contexts, report preparation time was reduced and efforts to improve data quality were supported, although the interrupted time series analysis did not show improved data quality. Twenty-three questionnaires were returned, revealing positive perceptions of ease of use and usefulness. In phase 5, the feasibility of conducting a cluster randomised controlled trial of QualDash was assessed. Interviews were undertaken to understand how QualDash could be revised to support a region-wide Gold Command. Requirements included multiple real-time data sources and functionality to help to identify priorities. Conclusions: Audits seeking to widen engagement may find the following strategies beneficial: involving a range of professional groups in choosing metrics; real-time reporting; presenting ‘headline’ metrics important to organisational-level staff; using routinely collected clinical data to populate data fields; and dashboards that help staff to explore and report audit data. Those designing dashboards may find it beneficial to include the following: ‘at a glance’ visualisation of key metrics; visualisations configured in line with existing visualisations that teams use, with clear labelling; functionality that supports the creation of reports and presentations; the ability to explore relationships between variables and drill down to look at subgroups; and low requirements for computing resources. Organisations introducing a dashboard may find the following strategies beneficial: clinical champion to promote use; testing with real data by audit staff; establishing routines for integrating use into work practices; involving audit staff in adoption activities; and allowing customisation. Limitations: The COVID-19 pandemic stopped phase 4 data collection, limiting our ability to further test and refine the QualDash theory. Questionnaire results should be treated with caution because of the small, possibly biased, sample. Control sites for the interrupted time series analysis were not possible because of research and development delays. One intervention site did not submit data. Limited uptake meant that assessing the impact on more measures was not appropriate. Future work: The extent to which national audit dashboards are used and the strategies national audits use to encourage uptake, a realist review of the impact of dashboards, and rigorous evaluations of the impact of dashboards and the effectiveness of adoption strategies should be explored. Study registration: This study is registered as ISRCTN18289782.