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The purpose of this study is to explore the feasibility of utilizing agricultural (almond shell, rice husk and wood) waste biochars for partial cement replacement by evaluating the relationships between the physiochemical properties of biochars and the early-age characteristics of cement pastes.

Biochars are prepared through the thermal decomposition of biomass in an inert atmosphere. Using varying percentages, biochars are used to replace ordinary Portland cement (OPC) in cement pastes at a water/binder ratio of 0.35. Characterization methods include XPS, FTIR, SEM, TGA, BET, Raman, loss-on-ignition, setting, compression and water absorption tests.

Accelerated setting in biochar-modified cement pastes is attributed to chemical interactions between surface functional groups of biochars and calcium cations from OPC, leading to the early development of metal carboxylate and alkyne salts, alongside the typical calcium-silicate-hydrate (C-S-H). Also, metal chlorides such as calcium chlorides in biochars contribute to the accelerate setting in pastes. Lower compression strength and higher water absorption result from weakened microstructure due to poor C-S-H development as the high carbon content in biochars reduces water available for optimum C-S-H hydration. Amorphous silica contributes to strength development in pastes through pozzolanic interactions. With its optimal physiochemical properties, rice-husk biochars are best suited for cement replacement.

While biochar parent material properties have an impact on biochar properties, these are not investigated in this study. Additional investigations will be conducted in the future.

Carbon/silicon ratio, oxygen/carbon ratio, alkali and alkaline metal content, chlorine content, carboxylic and alkyne surface functional groups and surface areas of biochars may be used to estimate biochar suitability for cement replacement. Biochars with chlorides and reactive functional groups such as C=C and COOH demonstrate potential for concrete accelerator applications. Such applications will speed up the construction of concrete structures and reduce overall construction time and related costs.

Reductions in OPC production and agricultural waste deterioration will slow down the progression of negative environmental and human health impacts. Also, agricultural, manufacturing and construction employment opportunities will improve the quality of life in agricultural communities.

Empirical findings advance research and practice toward optimum utilization of biomass in cement-based materials.

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.

This study aims to develop a comprehensive conceptual framework that serves as a foundation for identifying most critical delay risk drivers for Building Information Modelling (BIM)-based construction projects.

A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify key delay risk drivers in BIM-based construction projects that have significant impact on the performance of delay risk predictive modelling techniques.

The results show that contractor related driver and external related driver are the most important delay driver categories to be considered when developing delay risk predictive models for BIM-based construction projects.

This study contributes to the body of knowledge by filling the gap in lack of a conceptual framework for selecting key delay risk drivers for BIM-based construction projects, which has hampered scientific progress toward development of extremely effective delay risk predictive models for BIM-based construction projects. Furthermore, this study's analyses further confirmed a positive effect of BIM on construction project delay.

This study aims to examine the impact of extensible business reporting language (XBRL) technology and its functionality on various aspects of financial reporting and its overall quality.

To conduct this study, data was collected from a variety of professionals, including accountants, auditors, tax advisors and others. A structured research instrument was developed, and the collected data were analysed using structural equation modelling and mediation analysis techniques.

The study’s results showed that XBRL technology and its functionality have a noteworthy impact on different aspects of financial reporting. Moreover, the various aspects of financial reporting positively affect the overall quality of financial reporting.

This study solely relied on the opinions of various professionals regarding the current issue under investigation and did not empirically assess the reporting practices of companies by examining their XBRL-based reports. Additionally, it concentrated solely on financial reporting aspects and did not account for non-financial aspects. The main theoretical contributions of this paper to technology in financial reporting, XBRL and accounting literature are that it sheds light on the influence of the use of technologies in the business reporting process and their influence on various aspects of business reporting, which has only received confined focus from earlier studies so far.

This study’s findings could provide valuable insights to the managerial teams of organizations seeking to digitize their business reporting practices, specifically in areas such as regulatory compliance, integrated reporting and timely dissemination of reports in a sustainable way. Furthermore, it could help these teams reap the benefits of technology for various regulatory compliance matters.

This study could assist business organizations and regulatory authorities in adopting and implementing technology such as XBRL for accounting and business reporting. Furthermore, the study’s findings can aid in enhancing financial reporting practices by considering emerging aspects such as ESG and sustainability aspects.

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.

The study aims to analyse the role of digitisation in accounting in enhancing the overall effectiveness of accounting functions. To achieve this, the study provides empirical evidence from the stakeholder’s perspective of digitisation of accounting, auditing, reporting and regulatory compliance procedures.

The study has applied a quantitative approach to identify the thoughts of auditors, accountants and academicians on the impact of digitalised accounting applications on accounting functions. The data was collected by administering an empirical study and a sample of 482 professionals from the accounting, auditing and academic sectors. To analyse and interpret data descriptive statistics, structured equation modelling and mediation analysis has been used.

The finding of the study signifies the relevance of digitalised accounting applications in accounting functions and reveals that there is a significant impact of digitalisation on accounting, auditing, reporting and regulatory compliance aspects of accounting functions. The outcome of the study explores that a digitalised accounting system reduces possible errors and improves the accuracy and transparency of the system.

The study highlighted the importance of developing new methods and techniques that can be used in practice. This indirectly advocates the inclusion of such concepts in accounting curricula to emphasise the need to understand the challenges and opportunities created by digitisation. Furthermore, the study will become a motivation to scholars who intend to explore different areas through which new technologies can be adopted to transform traditional accounting systems.

The contributions of the current study have implications that the adoption of digitised accounting enhances economic efficiency through a reduction in accounting costs, and enhanced accuracy that leads to the elimination of penalties and litigations for non-compliance with regulatory authorities. This indirectly impacts positively on the financial health of the business organisations and economies at large. This implication becomes greater evidential support to the organisations which are yet to plan the adoption and implementation of digital tools in their organisation for accounting functions.

Digitalisation is a relevant part of the accounting function to improve efficiency and accuracy. Since accounting and auditing practitioners struggle to control the accuracy and efficiency of transactions. Furthermore, the outcome of the study assists organisations in gaining real-time access to financial data, transforms workflows and empowers management to make timely informed sound decisions, optimise resource allocation, efficient regulatory compliance and so on.

This study aims to explore the antecedents of donors’ attitudes toward fundraising campaigns to fight COVID-19 in the United Arab Emirates (UAE) during the pandemic crisis. This manuscript identified how moderating effects of ethical dimensions can strengthen the relationship between trust in charity and charity projects with their attitude to raise funds to mitigate pandemic repercussions.

This study follows a quantitative approach by administering survey instruments to collect the data from the sample of respondents. A total of 391 responses were obtained adopting snowball sampling and analyzed through structural equation modeling (SEM) to derive meaningful results for path analysis.

The findings of this study indicate that certain insights need to be considered to trigger the donors’ attitude toward raising or participating in charity-oriented campaigns, especially during pandemic situations. For instance, organizing more transformable processes in charity projects and establishing more trust factors among donors is highly essential in charity activities. Similarly, promoting ethical dimensions of the donors toward supporting the vulnerable more effectively and encouraging them to participate or organize philanthropic activities certainly benefit and support this noble cause.

This study will help the government and nonprofit organizations in devising their campaigns for raising funds. The findings of this study suggest that ethics is an important consideration and driver for donors in philanthropy-serving organizations and individuals.

This research contributes to the literature on donation and philanthropic studies focusing on fundraising campaigns attitudes during COVID-19. This study contributes influential factors and attitudes of individuals and organizations toward charity and philanthropic service.

This paper aims to establish the most underlying factors causing construction projects delay from the most applicable.

The paper conducted survey of experts using systematic review of vast body of literature which revealed 23 common factors affecting construction delay. Consequently, this study carried out reliability analysis, ranking using the significance index measurement of delay parameters (SIDP), correlation analysis and factor analysis. From the result of factor analysis, this study grouped a specific underlying factor into three of the six applicable factors that correlated strongly with construction project delay.

The paper finds all factors from the reliability test to be consistent. It suggests project quality control, project schedule/program of work, contractors’ financial difficulties, political influence, site conditions and price fluctuation to be the six most applicable factors for construction project delay, which are in the top 25% according to the SIDP score and at the same time are strongly associated with construction project delay.

This paper is recommending that prospective research should use a qualitative and inductive approach to investigate whether any new, not previously identified, underlying factors that impact construction projects delay can be discovered as it followed an inductive research approach.

The paper includes implications for the policymakers in the construction industry in Nigeria to focus on measuring the key suppliers’ delivery performance as late delivery of materials by supplier can result in rescheduling of work activities and extra time or waiting time for construction workers as well as for the management team at site. Also, construction stakeholders in Nigeria are encouraged to leverage the amount of data produced from backlog of project schedules, as-built drawings and models, computer-aided designs (CAD), costs, invoices and employee details, among many others through the aid of state-of-the-art data driven technologies such as artificial intelligence or machine learning to make key business decisions that will help drive further profitability. Furthermore, this study suggests that these stakeholders use climatological data that can be obtained from weather observations to minimize impact of bad weather during construction.

This paper establishes the three underlying factors (late delivery of materials by supplier, poor decision-making and Inclement or bad weather) causing construction projects delay from the most applicable.

The Finite Element Method and Response Surface Method are used to find the effect of milling parameters (Cutting speed, Feedrate and Axial depth) on plastic strain when milling Hastelloy C‐22HS. This simulation gain more understanding of the strain distribution in metal cutting. Response surface method (RSM) has been used to minimize the number of simulation. The contour plot from the RSM shows the relationship between variables (cutting speed, feedrate and axial depth) and response (plastic strain ‐ rate).The friction interaction along the tool‐chip interface is modeled with Coulomb friction law.