This paper provides a comprehensive review on the Artificial Intelligence (AI) based models for predictive maintenance (PdM) of water injection pumps (WIPs

Firms in the construction industry are under increasing pressure from regulatory bodies, clients, and the public to integrate sustainability into their business strategies. However, they encounter numerous barriers that hinder the implementation of environmental sustainability practices in construction project management. This study aimed to examine these barriers within the context of the United Arab Emirates’ (UAE’s) construction industry. To achieve this, the research employed a mixed-method approach. Initially, interviews were conducted to identify the prevalent barriers, resulting in the identification of twelve key barriers. Subsequently, a structured questionnaire was distributed to project managers from 90 firms, both ISO 14001-certified and non-certified, to rank these barriers and assess their significance. The findings revealed that “economic benefits placed above meeting environmental sustainability requirements” was the most critical barrier. Through factor analysis, three latent factors were extracted: (1) organizational and policy barriers, (2) compliance and resource efficiency barriers, and (3) sustainable design implementation barriers. Notably, significant differences were observed between ISO 14001-certified and non-certified firms, particularly regarding the importance of “economic benefits placed above meeting environmental sustainability requirements” and “insufficient consultation with stakeholders”. This study highlights the critical barriers to implementing environmental sustainability practices in the UAE’s construction industry and provides actionable suggestions for policymakers and decision-makers to overcome these challenges, with implications for similar environments worldwide.

The aim of this research is to investigate barriers and challenges associated with Artificial Intelligence (AI) adoption and implementation in the logistics operations. A systematic literature review was conducted to assess the major categories of challenges as well as specific challenges for each category published in peer-reviewed articles that are relevant to logistics operations. A total of 114 articles from six (6) electronic databases (i.e., IEEE Xplore, Science Direct, Elsevier's Scopus, Web of Science. Springer link, Google Scholar) were analyzed. Based on the findings, 7 major categories of barriers and challenges of AI adoption and implementation in the logistic are identified:(organisational and managerial challenges, technological challenges, economic challenges, data challenges, environmental challenges, social challenges, political, legal, and ethical challenges). Similarly, 16 specific challenges such as lack of top management support, internal requirement-organizational strategy, realtime response, disconnected systems, technology complexity, skill and expertise, high investment, operational costs, AI infrastructure, data standardization, scalability factors, data privacy and security, and insufficient operational warehouse space were identified. This research provides a further benchmark for decision making for logistics operation managers to deal with challenges and barriers in the adoption of AI in supply chain management.

Article Multi-Criteria Decision-Making for Selecting Renewable and Sustainable Gasoline Biofuel Additives Based on an Integrated AHP-TOPSIS Model Montaser Mahmoud 1, Tamer M. M. Abdellatief 1,2, Ridvan Aydin 3 and Mohammad Ali Abdelkareem 1,2,* 1 Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates 2 Chemical Engineering Department, Minia University, Elminia 61519, Egypt 3 Department of Industrial Engineering and Engineering Management, College of Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates * Correspondence: mabdulkareem@sharjah.ac.ae Received: 28 January 2025; Revised: 7 March 2025; Accepted: 8 April 2025; Published: 22 April 2025 The production of biofuel from locally available biomass resources is a crucial step toward achieving a sustainable energy production system. As a result, it is crucial to select a suitable biomass resource by considering its availability and combining several other factors simultaneously. Since conventional single-criteria decision-making techniques can no longer handle such complexity, multi-criteria decision-making (MCDM) is recommended. The current paper aims to apply MCDM to select renewable and sustainable gasoline biofuel additives to produce high-octane gasoline with high gasoline engine performance and low exhaust emissions based on an integrated AHP-TOPSIS model. The compared gasoline biofuel alternatives are isopropanol, ethanol, methanol, isobutanol, di-isobutylene, n-butanol, and (Di isopropyl ether) DIPE. Ten technical criteria that address various elements such as research octane number, motor octane number, density, Reid vapor pressure, boiling point temperature, auto-ignition temperature, heat of evaporation at 25 °C, Flashpoint, stoichiometric air-fuel ratio (AFR), and laminar flame speed are used in MCDM. The overall MCDM results revealed that isopropanol and ethanol achieved the highest rankings, which is consistent with the advantages and technical characteristics of the gasoline biofuel additives. The ranking of gasoline additives places isopropanol at the top with a score of 0.6576, primarily due to its anti-knock properties, which contribute to the formation of gasoline with high octane, which is environmentally in fuel blending. This was closely followed by ethanol and isobutanol, with scores of 0.6301 and 0.626, respectively.