Search results

This paper aims to examine the impact of the assurance and advisory role of internal audit (ADRIA) on organisational, human and technical proactive measures to enhance cybersecurity (CS).

The questionnaire was used to collect data for 97 internal auditors (IAu) from the Gulf Cooperation Council countries. The authors used partial least squares (PLS) to test the hypotheses.

The results show a positive effect of the ADRIA on each of the organisational proactive measures, human proactive measures and technical proactive measures to enhance CS. The study also found a positive effect of the confirmatory role of IA on both human proactive measures and technical proactive measures to enhance CS. No effect of the confirmatory role of IA on the organisational proactive measures is found.

This study focused on only three proactive measures to enhance CS, and this study was limited to the opinions of IAu. In addition, the study was limited to using regression analysis according to the PLS method.

The results of this study show that managers need to consider the influential role of IA as a value-adding activity in reducing CS risks and activating proactive measures. Also, IAu must expand its capabilities, skills and knowledge in CS auditing to provide a bold view of cyber threats. At the same time, the institutions responsible for preparing IA standards should develop standards and guidelines that help IAu to play assurance and advisory roles.

To the best of the authors’ knowledge, this is the first study of its kind that deals with the impact of the assurance and ADRIA on proactive measures to enhance CS. In addition, the study determines the nature of the advisory role and the assurance role of IA to strengthen CS.

The objective of this study was to delineate and compare enteral nutrition (EN) practices among neonatal units across the Arabian Gulf countries.

A cross-sectional study was conducted by recruiting 255 clinicians working in neonatal units in the Arabian Gulf countries.

Out of 255 invited clinicians, 73 (29%) participated in the survey. Neonatal units used varied EN strategies, where feeding practices exhibited variability. The majority (74%) of units had a local standard feeding protocol, while 18% followed international protocols, and 8% did not adhere to a specific protocol. When maternal milk was not used, the main alternatives were preterm formula (67%) and predigested formula (14%). The age at which the first EN was commenced and the reported advancement rate showed significant variations among different units (p < 0.001). The initiation of fortification was primarily driven by reaching a specific enteral volume (commonly reported as 100 mL/kg/day) and addressing poor postnatal growth. Fortification practices did not differ significantly among professions, except for the initial fortification strength, where none of the dietitians and only 8.3% of neonatologists preferred full strength, compared to 28.6% and 21.4% of medical residents and nurses, respectively (p = 0.033).

This study marks the first exploration of EN practices in neonatal units, examining their local and cross-country variations. It provides valuable insights to guide local trials and foster global collaboration among neonatal units to establish a unified knowledge base, standardized practices and promote research and innovation, ultimately contributing to optimal feeding practices for very preterm infants.

The purpose of this study is to investigate whether the lockdown and the increased spread of food delivery applications (FD Apps) during COVID-19 pandemic have augmented the consumption of fast foods.

A cross-sectional study was conducted on 673 adults from different regions of Saudi Arabia using an online questionnaire.

Data showed that 61% (N = 410) of participants used FD Apps during the pandemic. Among those users, 54.9% (225) were females and 70.5% were in the 18–44 years old group. Most FD Apps’ users were university graduates (74.4%). The increased use of FD Apps during the pandemic significantly affected the eating behavior and the nutritional pattern. It has further significantly augmented the consumption of fast foods (p-value < 0.05).

This study reports on the use of FD Apps during COVID-19 pandemic in Saudi Arabia and its impact on consumer eating pattern. This study shows the need for prudent use of these applications to limit ordering fast foods and consider healthier choices. It further calls for education programs, awareness campaigns, legislative measures and formal policies to rationalize the use of such applications for better nutrition, health and well-being.

The Saudi 2030 Housing Program Vision aims to increase the population of Riyadh City, the capital of the Kingdom of Saudi Arabia, to between 15 and 20 million people. This paper aims to predict the demand for residential units in Riyadh City by 2030 in line with this vision.

This paper adopts a statistical modeling approach to estimate the residential demands for Riyadh City. Several population growth models, including the nonlinear quadratic polynomial spline regression model, the sigmoidal logistic power model and the exponential model, are tested and applied to Riyadh to estimate the expected population in 2030. The growth model closest to the Kingdom’s goal of reaching between 15 and 20 million people in 2030 is selected, and the paper predicts the required number of residential units for the population obtained from the selected model. Desktop database research is conducted to obtain the data required for the modeling and analytical stage.

The exponential model predicts a population of 16,476,470 in Riyadh City by 2030, and as a result, 2,636,235 household units are needed. This number of housing units required in Riyadh City exceeds the available residential units by almost 1,370,000, representing 108% of the available residential units in Riyadh in 2020.

This study provides valuable insights into the demand for residential units in Riyadh City by 2030 in line with the Saudi 2030 Housing Program Vision, filling the gap in prior research. The findings suggest that significant efforts are required to meet the housing demand in Riyadh City by 2030, and policymakers and stakeholders need to take appropriate measures to address this issue.

This study evaluated the impact of a faculty training program on student assessment using the Kirkpatrick model.

A self-reported survey assessed 111 Saudi and non-Saudi participants' satisfaction. Subjective and objective measures (self-reported measures, assessment literacy inventory and performance-based assessment tasks) gauged participants' learning level. Pre- and post-training data were collected from 2020 to 2022.

A highly significant effect on satisfaction (>80%) and learning levels was observed, as manifested by workplace practices of student assessment (>70%, the cut-off score). Pre- and post-training comparisons of participants' satisfaction and assessment literacy scores showed significant improvements following training. Multiple regression analyses showed no significant effects for gender and educational attainment but a substantial impact of academic cluster on participants' student assessment skills.

Long-term effects of training faculty on assessment practices and student achievement will be studied at the institutional level in future research.

The current study contributes to human capital investment via faculty training on student assessment, helping them comply with assessment best practices. This assures the quality, fairness and consistency of assessment processes across disciplines in higher education institutions, enhances assessment validity and trust in educational services and may support institutional accreditation.

This study provides opportunities for sharing best practices and helps establish a community of practice. It enhances learning outcomes achievement and empowers higher education graduates with attributes necessary to succeed in the labor market. The human capital investment may have a long-term impact on overall higher education quality.

This study contributes to the scarce literature investigating the impact of training faculty from different clusters on student assessment using subjective and objective measures. It provides developing and evaluating a long-term student assessment program following the Kirkpatrick model.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

The target of this research is to develop a mathematical model which combines the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) and the Multi-Skilled Resource-Constrained Project Scheduling Problem (MSRCPSP). Due to the importance of resource management, the proposed formulation comprises resource leveling considerations as well. The model aims to simultaneously optimize: (1) the total time to accomplish all projects and (2) the total deviation of resource consumptions from the uniform utilization levels.

The K-Means (KM) and Fuzzy C-Means (FCM) clustering methods have been separately applied to discover the clusters of activities which have the most similar resource demands. The discovered clusters are given to the scheduling process as priori knowledge. Consequently, the execution times of the activities with the most common resource requests will not overlap. The intricacy of the problem led us to incorporate the KM and FCM techniques into a meta-heuristic called the Bi-objective Symbiosis Organisms Search (BSOS) algorithm so that the real-life samples of this problem could be solved. Therefore, two clustering-based algorithms, namely, the BSOS-KM and BSOS-FCM have been developed.

Comparisons between the BSOS-KM, BSOS-FCM and the BSOS method without any clustering approach show that the clustering techniques could enhance the optimization process. Another hybrid clustering-based methodology called the NSGA-II-SPE has been added to the comparisons to evaluate the developed resource leveling framework.

The practical importance of the model and the clustering-based algorithms have been demonstrated in planning several construction projects, where multiple water supply systems are concurrently constructed.

Reviewing the literature revealed that there was a need for a hybrid formulation that embraces the characteristics of the RCMPSP and MSRCPSP with resource leveling considerations. Moreover, the application of clustering algorithms as resource leveling techniques was not studied sufficiently in the literature.

This study aims to develop a constructability index (CI) that can ease the construction activities in a project based on the contractors’ experience and resources. The proposed CI is a vital decision support tool that quantifies the difficulty level for the contractor to execute certain activities with the contingency of other project elements. The virtual reality (VR) technology was used to provide additional data, communicate the contingency impact of other project elements on specific activities and provide sequential execution data to the contractors. This can minimize the risk of not being able to execute various activities on time and within the budget.

The VR-based CI was developed through two steps. Step 1 was to identify the factors affecting constructability by exploring the literature and consulting local construction experts. These factors were then organized through a hierarchy of main factors and subfactors and validated by local experts through predesigned surveys. The factors were classified into VR dependent or non-VR independent, and their relative weights were calculated using the analytical hierarchy process along with their reliability, which was determined using Cronbach’s alpha approach. Step 2 was to define the attributes for the constructability factors defined in Step 1 using the Multi Attribute Utility Theory to quantify the contractor’s compliance level of these factors by giving them the appropriate score. The utility factors for the VR-independent factors were obtained through standards, literature and local surveys, and they were quantified on a 1–10 scale. However, the VR-dependent factors were given their corresponding scores using the developed VR navigation environment generated by integrating Autodesk Revit and Navisworks software. Accordingly, the CI for each activity was evaluated, and the overall CI for the project was calculated by aggregating the CIs for all activities.

The developed CI quantifies the contractor’s ability to execute construction projects and addresses the lack of communication and coordination between the various construction units in the planning phase itself. Moreover, it can resolve possible hard (physical) and soft (time) construction clashes and minimize their impacts on project schedule and budget. Among the relative weights of the identified factors, prefabrication of building components was found to have the highest effect on constructability. Furthermore, applying the developed VR-CI, a real project showed that the utility values of the main factors quantified on a ten-point scale were between 6 and 9, which means routine supervisions and monitoring are required.

Though the concepts of constructability and VR have been used in different contexts, their integration to develop a comprehensive CI for the building construction industry is a unique contribution, which has not been reported previously.

The author’s interest in vernacular architecture, over the years, has attracted the author’s attention to three distinctive and similar forms of architecture in faraway regions of the globe. These are; Asir region of Saudi Arabia, The Caucasus including the republic of Georgia, Chechenia, and North Ossetia, and Sichuan region in China. Stone towers dominate the landscape of these remote regions. The similarity of these towers in these far away regions is quite remarkable.

This paper will introduce these towers in their geographic, social and natural context. Although several studies have been done on these regions, it is the aim of this paper to introduce their architecture in a comparative approach to explore how these remotes corners of the globe with different historical, ethnic and cultural backgrounds developed similar architectural forms in total isolation from each other.

Architecture is a physical production of different and diverse factors. Geographically, isolated regions with similar natural and social factors, mountainous landscape, tribally-based, agrarian societies, produces similar architectural forms.

This paper is a clear testimony to the human nature and how people think, react and build, under similar conditions. Architecture becomes a manifestation of human oneness, unity, believes and behaviour.

This paper aims to propose a novel way of using textual clustering as a feature selection method. It is applied to identify the most important keywords in the profile classification. The method is demonstrated through the problem of sick-leave promoters on Twitter.

Four machine learning classifiers were used on a total of 35,578 tweets posted on Twitter. The data were manually labeled into two categories: promoter and nonpromoter. Classification performance was compared when the proposed clustering feature selection approach and the standard feature selection were applied.

Radom forest achieved the highest accuracy of 95.91% higher than similar work compared. Furthermore, using clustering as a feature selection method improved the Sensitivity of the model from 73.83% to 98.79%. Sensitivity (recall) is the most important measure of classifier performance when detecting promoters’ accounts that have spam-like behavior.

The method applied is novel, more testing is needed in other datasets before generalizing its results.

The model applied can be used by Saudi authorities to report on the accounts that sell sick-leaves online.

The research is proposing a new way textual clustering can be used in feature selection.