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Delay in housing adaptation is a major problem, especially in assessing if homes are suitable for the occupants and in determining if the occupants are qualified for the Disabled Facilities Grant (DFG). This paper describes the development of two self-administered intelligent integrated assessment tools from the DFG Adapt-ABLE system: (1) The Home Suitability Assessment Platform, which is a preventive mechanism that allows assessment of the suitability of homes based on occupants’ mobility status and (2) an indicative assessment platform that determines if the applicants are qualified for the DFG to prevent lengthy delays.

The adopted method aligned with a development study approach: a grounded literature review, a severity measurement approach, two stakeholder engagement workshops, four brainstorming sessions and four focus group exercises. The system development relied on Entity–Relationship Diagram (ERD) technique for data structures and database systems design. It uses DFG context sensitivity with alignment with DFG guidance, interlinkages and interoperability between the assessment tools and other platforms of the integrated Adapt-ABLE system.

The assessment tools are client-level outcomes related to accessibility, usability and activity based on the assessment process. The home suitability platform shows the percentage of the suitability of a home with assessment results that suggest appropriate action plans based on individual mobility status. The indicative assessment combines the function of referral, allocation, assessment and test of resources into an integrated platform. This enables timely assessment, decision-making and case-escalation by Occupational Therapists based on needs criteria and the eligibility threshold.

These assessment tools are useful for understanding occupants’ perception of their physical housing environment in terms of accessibility, suitability and usability based on basic activities of daily living and their mobility status. The indicative self-assessment tool will substantially cut down the application journey. The developed tools have been recommended for use in the CSJ Disability Commission report and the UK government Guidance on DFGs for local authorities in England.

Due to the practical complexity and fragmented nature of the construction industry, digitalisation, like other innovations, is not easily achieved. This study aims to explore organisational influences on digitalisation within construction firms.

The study uses structured open-ended questions as a data collection tool for a qualitative investigation. The qualitative approach enabled participants to express their inputs and maximise the diversity of data, offering new insights and discussions that are distinct from previous works.

Construction professionals from 22 organisations provided their perspectives on digital transformation and their organisations. Under four constructs – structure, culture, leadership and internal processes, findings uncovered 16 determinants critical to digitalisation in construction firms. The study offers a theoretical perspective supported by empirical data to explore the complex dynamics and internal interactions of organisational influence on the uptake of digitalisation in the construction industry.

This paper offers arguments from a theoretical lens by applying the organisational influence model and capturing the variables under each construct in an exploratory manner to highlight the reasoning behind the low digital uptake in construction firms. This research aids academia and practice on the pressure points responsible for enhancing, or undermining, digital uptake in construction firms at an organisational level.

The lack of a proper register to store, match and display information on the adapted property has led to a waste of resources and prolonged delays in matching the disabled and elderly people with appropriate properties. This paper presents the development of a Housing Adaptations Register with user-matching functionalities for different mobility categories. The developed system accurately captures and documents adapted home information to facilitate the automated matching of disabled/aged applicants needing an adapted home with suitable property using banding, mobility and suitability index.

A theoretical review was conducted to identify parameters and develop adaptations register construct. A survey questionnaire approach to rate the 111 parameters in the register as either moderate, desirable or essential before system development and application. The system development relied on DSS modelling to support data-driven decision-making based on the decision table method to represent property information for implementing the decision process. The system is validated through a workshop, four brainstorming sessions and three focus group exercises.

Development of a choice-based system that enables the housing officers or the Housing Adaptations Register coordinators to know the level of adaptation to properties and match properties quickly with the applicants based on their mobility status. The merits of the automated system include the development of a register to capture in real-time adapted home information to facilitate the automated matching of disabled/aged applicants. A “choice-based” system that can map and suggest a property that can easily be adapted and upgraded from one mobility band to the other.

The development of a housing adaptation register helps social housing landlords to have a real-time register to match, map and upgrade properties for the most vulnerable people in our society. It saves time and money for the housing associations and the local authorities through stable tenancy for adapted homes. Potentially, it will promote the independence of aged and disabled people and can reduce their dependence on social and healthcare services.

This system provides the local authorities with objective and practical tools that may be used to assess, score, prioritise and select qualified people for appropriate accommodation based on their needs and mobility status. It will provide a record of properties adapted with their features and ensure that matching and eligibility decisions are consistent and uniform.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

Notwithstanding the Geographical Information System (GIS) being a fast-emerging green area of a digital revolution, the available studies focus on different subject areas of application in the construction industry, with no study that clarifies its knowledge strands. Hence, this systematic review analyses GIS core area of application, its system integration patterns, challenges and future directions in the construction industry.

A systematic review approach was employed, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. A total of 60 articles published between 2011 and 2022 were identified, thoroughly reviewed and analysed using thematic analysis.

The analysis revealed spatial planning and design, construction-task tracking, defect detection and safety monitoring as its four main application-based areas. The findings showed that the adoption of GIS technology is rapidly expanding and being utilised more in building projects to visual-track construction activities. The review discovered an integrated pattern involving data flow from a device and window-form application to GIS, the pathways to data exchange between platforms to platforms, where ArcGIS is the most used software. Furthermore, the study highlighted the lack of interoperability between heterogeneous systems as the crux impediment to adopting GIS in the built environment.

The research provides a deep insight into possible areas where GIS is adopted in the construction industry, identifying areas of extensive and limited application coverage over a decade. Besides, it demystifies possible pathways for future integration opportunities of GIS with other emerging technologies within the construction industry.

This study explores the pivotal role of artificial intelligence (AI) in revolutionizing knowledge organization within Nigerian libraries. The purpose of this study is to assess the challenges faced by these libraries, propose strategic approaches for successful AI integration and highlight the potential benefits and future directions of this transformative journey.

This study uses a comprehensive review of existing literature, case studies and a qualitative analysis of challenges faced by Nigerian libraries. Strategies for AI integration are proposed based on targeted capacity building, collaborative partnerships and phased implementation approaches. The methodology also involves assessing the current landscape of AI in Nigerian academic libraries, examining applications and exploring the perceived impacts of AI on library services.

Nigerian libraries face challenges such as limited resources, outdated systems and diverse information that hinder traditional knowledge organization methods. The integration of AI offers dynamic solutions, streamlining administrative tasks, optimizing search algorithms and enhancing user engagement. The findings of this study emphasize the potential benefits of AI, including improved accessibility, searchability and long-term efficiency gains in library collections.

This research contributes to the existing literature by providing insights into the specific challenges faced by Nigerian libraries and proposing practical strategies for AI integration. This study emphasizes the transformative potential of AI in addressing immediate challenges and unlocking enduring benefits. The originality lies in the context-specific exploration of AI in Nigerian libraries, offering a roadmap for stakeholders to embrace technological advancements and position libraries as leaders in providing innovative knowledge services.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.