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This study aims to investigate the extent of Shariah compliance in wakalah sukuk and Shariah non-compliant risk disclosure in the sukuk documents and to analyse the risk management techniques associated with the disclosed risks.

This study uses qualitative document analysis as both data collection and analysis methods. The document analysis acts as a data collection method for 23 wakalah sukuk documents selected from 32 issuances of wakalah sukuk from 2017 to 2021. These sukuk documents were selected based on their availability from relevant websites. Document analysis, both content analysis and thematic analysis, were used to analyse the data. Codes were grounded from that data through keywords search of Shariah noncompliant risk and its risk management. Besides these, interviews were also conducted with four active industry players, i.e. two legal advisors of wakalah sukuk, a wakalah sukuk trustee and a sukuk institutional issuer. These interview data were analysed based on categorical themes, on the aspects of the extent of Shariah compliance in sukuk, and the participant’s views on the risk management techniques associated with the risks or used in the sukuk documents.

Overall, the findings reveal three types of Shariah non-compliant risks disclosed in the sukuk documents and seven risk management techniques associated with them. However, the disclosure and the risk management techniques can be considered minimal in contrast to the extent of Shariah compliance in a sukuk, i.e. Shariah compliance at the pre-issuance stage, ongoing stage and post-issuance stage. On top of these, it was also found from the interviews that not all risk management techniques are workable to manage Shariah non-compliant risk in sukuk. As a result, these findings suggest rigorous reviews of the existing Shariah non-compliance risk (SNCR) disclosures and risk management techniques by the relevant parties.

Sukuk documents used in the study are limited to corporate wakalah sukuk issued in Malaysia. Out of 32 issuances from 2015 to 2021, only 23 documents are available in relevant website. Thus, Shariah non-compliant risk disclosure and its risk management techniques analysed in this study are only limited in those documents.

The findings of this study suggest rigorous reviews on the existing Shariah non-compliance disclosures and risk management techniques. Other than these, future research in relation to uncommon risk management clauses, i.e. assurance, Shariah waiver and transfer of risk, are needed.

The insights presented in the analysis are of importance to sukuk issuers and the sukuk due diligence working group in enhancing the sukuk Shariah compliance and Shariah non-compliant risks disclosure and towards sukuk investors, in capturing and assessing Shariah non-compliant risks in a sukuk and to assist them to make informed investment decisions. More importantly, this study has found few areas of future study in relation to SNCR disclosures and SNCR risk management techniques.

With the development of science and technology, more creators are trying to use new crafts to represent the cultural trends of the social media era, which makes cultural heritage innovative and new genres emerge. This compels the academic community to examine craft from a new perspective. It is very helpful to understand the hidden representational structure of craft more deeply and improve the craft innovation system of cultural and creative products that we deconstruct the craft based on Complex Network and discover its intrinsic connections.

The research crawled and cleaned the craft information of the top 20% products on the Forbidden City’s cultural and creative products online and then performed Complex Network modeling, constructed three craft representation networks among function, material and technique, quantified and analyzed the inner connections and network structure of the craft elements, and then analyzed the cultural inheritance and innovation embedded in the craft representation networks.

The three dichotomous craft representation networks constructed by combining function, material and technique: (1) the network density is low and none of them has small-world characteristics, indicating that the innovative heritage of the craft elements in the Forbidden City’s cultural and creative products is at the stage of continuous exploration and development, and multiple coupling innovation is still insufficient; (2) all have scale-free characteristics and there is still a certain degree of community structure within each network, indicating that the coupling innovation of craft elements of the Forbidden City’s cultural and creative products is seriously uneven, with some specific “grammatical combinations” and an Island Effect in the network structure; (3) the craft elements with high network centrality emphasize the characteristics of decorative culture and design for the masses, as well as the pursuit of production efficiency and economic benefits, which represent the aesthetic purport of contemporary Chinese society and the ideological trend of production and life.

The Forbidden City’s cultural and creative products should continue to develop and enrich the multi-coupling innovation of craft elements, clarify and continue their own brand unique craft genes, and make full use of the network important nodes role.

In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.

A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.

The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.

The research was limited to the findings from the bibliometric literature review.

The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.

This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

This study aims to identify and evaluate the key strategies to promote the implementation of automation techniques with reference to the Nigerian construction industry.

Pragmatic philosophical thinking using a mixed-method approach (a combination of qualitative and quantitative) was adopted for this study. The qualitative strand of this research was achieved using a Delphi technique while a well-structured questionnaire conducted among 191 construction professionals was adopted to attain the quantitative strand. Obtained data were analyzed using frequencies, percentages, mean item scores, Kruskal–Wallis H test and exploratory factor analysis (FA).

Results revealed that the “provision of funding and subsidies for automation techniques” “mandatory automation policies and regulations,” “creating incentives for adoption,” “formulation of programs to promote awareness” and “deploying gamification to boost employee performance” were the top five strategies to promote the adoption of automation techniques. FA revealed four principal clusters, namely, awareness and publicity programs, government regulations and standards, provision of education and training and awards and recognition.

This study provided a solid theoretical and empirical foundation that can be useful to construction industry stakeholders, decision-makers, policymakers and the government in mapping out strategies to promote the incorporation and deployment of automation and robotics in the construction industry.

To the best of the authors’ knowledge, this study is one of the first in developing countries and Nigeria to establish an ordered grouping structure of the strategies to promote the adoption of automation techniques.

The work on bridge resilience assessment includes quantitative and qualitative approaches to compare the multiple bridges based on their resilience. But still, the bridge resilience obtained by these assessment approaches is inefficient when prioritising multiple bridges to improve their resilience. Therefore, this study aims to develop a methodology for prioritising the bridges to improve their resilience.

The research methodology follows three sequential phases. In the first phase, criteria importance through intercriteria correlation (CRITIC) technique is used to compute the criteria weights. The criteria considered are age, area, design high flood level, finish road level FRL and resilience index of bridges. While 12 river-crossing bridges maintained by one bridge owner are considered as alternatives. Then, in the second phase, the prioritisation of each bridge is evaluated using five techniques, including technique for order of preference by similarity to ideal solution, VIKOR (in Serbian, Visekriterijumska Optimizacija I Kompromisno Resenje), additive ratio assessment, complex proportional assessment and multi-objective optimisation method by ratio analysis. Finally, in the third phase, the results of all five techniques are integrated using CRITIC and the weighted sum method.

The result of the study enables bridge owners to deal with the particular bridge that requires resilience improvement. The study concluded that it is not enough to consider only the bridge resilience index to improve its resilience. The prioritisation exercise should consider various other criteria that are not preferred during the bridge resilience assessment process.

The proposed methodology is a novel framework based on the existing multi-criteria decision-making (MCDM) techniques for contributing knowledge in the domain of bridge resilience management. It can efficiently overcome the pitfall of decision-making when two bridges have the same resilience index score.

The purpose of this manuscript, a state feedback gain depends on the optimal design of fractional order PID controller to time-delay system is established. In established optimal design known as advanced cuttlefish optimizer and random decision forest that is combined performance of random decision forest algorithm (RDFA) and advanced cuttlefish optimizer (ACFO).

The proposed ACFO uses the concept of crossover and mutation operator depend on position upgrading to enhance its search behavior, calculational speed as well as convergence profile at basic cuttlefish optimizer.

Fractional order proportional-integrator-derivative (FOPID) controller, apart from as tuning parameters (kp, ki and kd) it consists of two extra tuning parameters λ and µ. In established technology, the increase of FOPID controller is adjusted to reach needed responses that demonstrated using RDFA theory as well as RDF weight matrices is probable to the help of the ACFO method. The uniqueness of the established method is to decrease the failure of the FOPID controller at greater order time delay method with the help of controller maximize restrictions. The objective of the established method is selected to consider parameters set point as well as achieved parameters of time-delay system.

In the established technique used to evade large order delays as well as reliability restrictions such as small excesses, time resolution, as well as fixed condition defect. These methods is implemented at MATLAB/Simulink platform as well as outcomes compared to various existing methods such as Ziegler-Nichols fit, curve fit, Wang method, regression and invasive weed optimization and linear-quadratic regression method.

This paper aims to identify modern construction techniques for affordable housing, such as prefabrication and interlocking systems, that can save time and cost while also providing long-term sustainable benefits that are desperately needed in today's construction industry.

The need for housing is growing worldwide, but traditional construction cannot cater to the demand due to insufficient time. There should be some paradigm shift in the construction industry to supply housing to society. This paper presented a state-of-the-art review of modern construction techniques practiced worldwide and their advantages in affordable housing construction by conducting a systematic literature review and applying the backward snowball technique. The paper reviews modern prefabrication techniques and interlocking systems such as modular construction, formwork systems, light gauge steel/cold form steel construction and sandwich panel construction, which have been globally well practiced. It was understood from the overview that modular construction, including modular steel construction and precast concrete construction, could reduce time and costs efficiently. Further enhancement in the quality was also noticed. Besides, it was observed that light gauge steel construction is a modern phase of steel that eases construction execution efficiently. Modern formwork systems such as Mivan (Aluminium Formwork) have been reported for their minimum construction time, which leads to faster construction than traditional formwork. However, the cost is subjected to the repetitions of the formwork. An interlocking system is an innovative approach to construction that uses bricks made of sustainable materials such as earth that conserve time and cost.

The study finds that the prefabrication techniques and interlocking system have a lot of unique attributes that can enable the modern construction sector to flourish. The study summarizes modern construction techniques that can save time and cost, enhancing the sustainability of construction practices, which is the need of the Indian construction industry in particular.

This study is limited to identifying specific modern construction techniques for time and cost savings, lean concepts and sustainability which are being practiced worldwide.

Modern formwork systems such as Mivan (Aluminium Formwork) have been reported for their minimum construction time which leads to faster construction than traditional formwork.

The need for housing is growing rapidly all over the world, but traditional construction cannot cater to the need due to insufficient time. There should be some paradigm shift in the construction industry to supply housing to society.

This study is unique in identifying specific modern construction techniques for time and cost savings, lean concepts and sustainability which are being practiced worldwide.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.