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This research paper investigates the factors influencing wind energy adoption and sustainability in the United Arab Emirates (UAE). The investigation is motivated by a research gap, as there is limited existing research on the specific factors influencing the adoption of wind energy in the UAE. Understanding these factors is identified as a crucial problem for the successful transition to sustainable energy sources in the nation. Employing a systematic review methodology, data spanning from 1999 to 2023 across six databases were collected using specified keywords. A total of 203 articles were screened, resulting in 64 selected articles. The study categorizes derived factors into technological innovation, environmental impact, grid integration, and socioeconomic aspects. Utilizing a structured methodology involving questionnaire surveys, structural equation modeling (SEM), and multiple linear regression (MLR) analyses. 75 % of hypotheses were accepted, affirming significant positive relationships between technological innovation, environmental impact, grid integration, wind energy adoption, and sustainability were confirmed. While socioeconomic factors exhibit a nuanced influence, the findings underscore the UAE's remarkable progress in harnessing wind energy for a sustainable future. These findings underscore the importance of further investigating these factors and sub-factors for successfully transitioning to sustainable energy sources in the UAE.

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CCi) of 0.8525, followed by those related to digital fitness (CCi = 0.6481) and digital readiness (CCi = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure.

This article contributes to the existing literature by modeling and automating the learning process from previous aviation construction projects (ACPs) using artificial intelligence tools, where it will be easier to characterize aviation construction projects and identify the specifications of different aspects of the projects throughout their entire life cycle. An artificial intelligence (AI) framework is proposed for the categorization of aviation construction projects using different machine-learning (ML) methods with a focus on the UAE as a source of data. Airport construction projects have been seen to share a good deal of similar attributes, which should simplify the decision-making process regarding layouts, design, equipment, labor, budget, complexity, etc. However, the gap in reality is that the huge and scattered sources of data, project specifications, characteristics, and the knowledge from past projects are not utilized in an automated way that could simplify the navigation through projects for better future decision-making. The utilization of AI/ML tools is expected to be useful here in order to reduce the revisions of design and construction rework by classifying the projects and the elements that managers need to consider. The planning, design, and construction of new projects can be improved by identifying the attributes of past projects and categorizing them according to similarities, differences, and complexities. Specifically speaking, a framework of hierarchical clustering and neural networks is integrated together to form the classification model. Upon implementing hierarchical classification and neural networks, it was found that neural networks could demonstrate remarkable classification results; the error in classification was minimal in most of the cases. The advantage of such classification is to help decision-makers utilize best practice from the groups of previous projects, which were classified using both hierarchical and neural networks models. With this classification, rework can be minimized, overhead costs may be reduced, and past best practices can be utilized.

Surface mining is one of the hazardous industries that have several risky operations, including transportation, treatment, and mineral extraction. To avoid the risk of disaster, it is important to evaluate safety procedures and determine expected hazards. The aim of this study is to develop a thorough safety evaluation model for the surface mining industry based on the analytic hierarchy process (AHP), one important multi-criteria decision-making approach. A total of 11 criteria and 36 sub-criteria that are both independent and homogeneous were involved in the decision problem. Further, a deep sensitivity analysis was conducted to assess the stability of the ranking preference. The findings indicate that four out of the eleven criteria are particularly significant. To test the model’s applicability and effectiveness, a case study was conducted involving three surface mining companies located in the north of Jordan. The results demonstrate that the model is reliable, applicable, and effective in addressing real-world problems.

Implementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering. This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. This research paper’s main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award, hence saving time and cost. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.