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This study aims to explore the instructional leadership practices implemented by academic professionals and leaders to enhance the curriculum of Islamic economics and finance (IEF) in two public universities in Saudi Arabia.

This study used a qualitative approach, using a case study methodology that focused on two meticulously chosen universities in Saudi Arabia. A total of 21 academics from two public universities in Saudi Arabia who worked in IEF schools were selected for semi-structured interviews.

The findings showed that two universities in Saudi Arabia that offer degrees in IEF exhibited limited instructional leadership. The findings indicate four apparent barriers that may explain the lack of involvement in instructional leadership and fair practices in the IEF curriculum at Saudi Arabian universities. According to this study, a positive collegial climate in Saudi universities’ IEF promotes shared instructional leadership.

The use of a limited qualitative method and small sample of respondents in this study may not provide enough evidence to generalise the findings to all universities and higher education schools in Saudi Arabia. Although a case study was used to describe IEF curriculum management and implementation at the two universities, caution should be exercised when applying these findings to other institutions.

IEF schools in Saudi universities need to leverage their positive, collaborative and relationship-building environments to develop activities that promote shared instructional leadership.

The research findings can offer valuable insights and examples for school leaders to develop instructional activities and promote the concept of “shared instructional leadership”. This approach involves delegating responsibilities and actions to others to enhance the IEF curriculum’s quality. Policymakers and university officials can use these findings to enhance strategic policies.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

The purpose of this study is to develop a decision-support framework that can be used by decision-makers to suspend public infrastructure projects. Additionally, the study also investigates how to select the most convenient infrastructure project for suspension.

The proposed framework includes an extensive set of factors and a novel comparison mechanism that can reveal the most convenient infrastructure project to be suspended. A comprehensible literature review and focus group discussion (FGD) sessions were conducted to identify factors that should be considered for suspension. Then, the neutrosophic analytic hierarchy process (N-AHP) method was used to determine the relative importance of the factors. Finally, the proposed comparison mechanism was demonstrated through a hypothetical case study and Technique for order of preference by similarity to ideal solution (TOPSIS) analysis.

Results showed that suspension decisions cannot be made merely based on “financial” factors. Instead, the other aspects, namely “Technical and managerial” and “Social and Environmental”, should also be taken into consideration. Second, factors related to the initial investment, cost of refinancing, cash flow, permits and approvals, insufficiency of bidders, degradation of the components, reputation, impact on stakeholders and criticality of the infrastructure were particularly elaborated as the most significant, needing the utmost attention of the decision-makers. Lastly, the results demonstrated that the proposed comparison mechanism has considerable potential to identify the most convenient infrastructure project for suspension.

Public infrastructure projects are often under pressure due to the inflationary state and economic stagnation of countries after major crises. The suspension decision for infrastructure projects necessitates comprehensible assessments to consider all consequences. Studies have widely investigated the contractual and legal aspects of project suspension in light of existing literature. However, little effort has been devoted to identifying the factors that decision-makers should consider before suspending a particular infrastructure project. Furthermore, existing literature does not investigate how to select the most convenient infrastructure project for suspension either. Thus, by developing a specific suspension framework for infrastructure projects by considering various factors, this study is the earliest attempt to examine the contract suspension mechanism of public infrastructure projects. In this respect, the study significantly contributes to the theory of contract management domain and has important managerial implications.