Staff publications (MMD)

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Open Access
A comparative analysis of circular economy practices in Saudi Arabia
(MDPI, 2025-04-08) Alsaud, Khalid; Assad, Fadi; Patsavellas, John; Salonitis, Konstantinos
The rise in urbanisation and resource consumption has highlighted the urgent need for sustainable economic models. The traditional linear economy, which relies heavily on non-renewable resources, exceeds the Earth’s capacity and poses significant sustainability challenges. As a result, there is an increasing necessity to transition towards a circular economy (CE) as a more sustainable alternative. Saudi Arabia, one of the world’s largest economies, is striving to implement this shift due to considerable environmental and economic challenges. However, the country currently lacks a dedicated circular economy strategy, which hinders its efforts to address issues such as waste management and excessive consumption. To bridge this gap, a comprehensive framework was developed to assess and compare Saudi Arabia’s circular economy initiatives, strategies, and policies with those of China, Japan, and Europe. Data were collected and analysed using thematic analysis, allowing for the identification of key similarities and differences between these regions. The study revealed notable variations in policies and practices, highlighting best practices that Saudi Arabia could adopt to strengthen its sustainability efforts. The findings underscore the importance of incorporating global best practices while tailoring strategies to the Kingdom’s specific needs. Policymakers and researchers in Saudi Arabia can utilise these insights to support a more effective transition towards a circular economy. Future research could adopt a quantitative approach, using indicators and metrics to enhance the impact of these findings.
Open Access
AI-driven maintenance optimisation for natural gas liquid pumps in the oil and gas industry: a digital tool approach
(MDPI, 2025-05-01) Almuraia, Abdulmajeed; He, Feiyang; Khan, Muhammad
Natural Gas Liquid (NGL) pumps are critical assets in oil and gas operations, where unplanned failures can result in substantial production losses. Traditional maintenance approaches, often based on static schedules and expert judgement, are inadequate for optimising both availability and cost. This study proposes a novel Artificial Intelligence (AI)-based methodology and digital tool for optimising NGL pump maintenance using limited historical data and real-time sensor inputs. The approach combines dynamic reliability modelling, component condition assessment, and diagnostic logic within a unified framework. Component-specific maintenance intervals were computed using mean time between failures (MTBFs) estimation and remaining useful life (RUL) prediction based on vibration and leakage data, while fuzzy logic- and rule-based algorithms were employed for condition evaluation and failure diagnoses. The tool was implemented using Microsoft Excel Version 2406 and validated through a case study on pump G221 in a Saudi Aramco facility. The results show that the optimised maintenance routine reduced the total cost by approximately 80% compared to conventional individual scheduling, primarily by consolidating maintenance activities and reducing downtime. Additionally, a structured validation questionnaire completed by 15 industry professionals confirmed the methodology’s technical accuracy, practical usability, and relevance to industrial needs. Over 90% of the experts strongly agreed on the tool’s value in supporting AI-driven maintenance decision-making. The findings demonstrate that the proposed solution offers a practical, cost-effective, and scalable framework for the predictive maintenance of rotating equipment, especially in environments with limited sensory and operational data. It contributes both methodological innovation and validated industrial applicability to the field of maintenance optimisation.
Open Access
Applications of large language models and multimodal large models in autonomous driving: a comprehensive review
(MDPI, 2025-04-01) Li, Jing; Li, Jingyuan; Yang, Guo; Yang, Lie; Chi, Haozhuang; Yang, Lichao
The rapid development of large language models (LLMs) and multimodal large models (MLMs) has introduced transformative opportunities for autonomous driving systems. These advanced models provide robust support for the realization of more intelligent, safer, and efficient autonomous driving. In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems. First, we provide an overview of the evolution of LLMs and MLMs, along with a detailed analysis of the architecture of autonomous driving systems. Next, we explore the applications of LLMs and MLMs in key components such as perception, prediction, decision making, planning, multitask processing, and human–machine interaction. Additionally, this paper reviews the core technologies involved in integrating LLMs and MLMs with autonomous driving systems, including multimodal fusion, knowledge distillation, prompt engineering, and supervised fine tuning. Finally, we provide an in-depth analysis of the major challenges faced by autonomous driving systems powered by large models, offering new perspectives for future research. Compared to existing review articles, this paper not only systematically examines the specific applications of LLMs and MLMs in autonomous driving systems but also delves into the key technologies and potential challenges involved in their integration. By comprehensively organizing and analyzing the current literature, this review highlights the application potential of large models in autonomous driving and offers insights and recommendations for improving system safety and efficiency.
Open Access
High foot traffic power harvesting technologies and challenges: a review and possible sustainable solutions for Al-Haram Mosque
(MDPI, 2025-04-11) Alotibi, Fatimah; Khan, Muhammad
The growing global demand for sustainable energy solutions has led to increased interest in kinetic energy harvesting as a viable alternative to traditional power sources. High-foot-traffic environments, such as public spaces and religious sites, generate significant mechanical energy that often remains untapped. This study explores energy-harvesting technologies applicable to public areas with heavy foot traffic, focusing on Al-Haram Mosque in Saudi Arabia—one of the most densely populated religious sites in the world. The research investigates the potential of piezoelectric, triboelectric, and hybrid systems to convert pedestrian foot traffic into electrical energy, addressing challenges such as efficiency, durability, scalability, and integration with existing infrastructure. Piezoelectric materials, including PVDF and BaTiO3, effectively convert mechanical stress from footsteps into electricity, while triboelectric nanogenerators (TENGs) utilize contact electrification for lightweight, flexible energy capture. In addition, this study examines material innovations such as 3D-printed biomimetic structures, MXene-based composites (MXene is a two-dimensional material made from transition metal carbides, nitrides, and carbonitrides), and hybrid nanogenerators to improve the longevity and scalability of energy-harvesting systems in high-density footfall environments. Proposed applications for Al-Haram Mosque include energy-harvesting mats embedded with piezoelectric and triboelectric elements to power IoT devices, LED lighting, and environmental sensors. While challenges remain in material degradation, scalability, and cost, emerging hybrid systems and advanced composites present a promising pathway toward sustainable, self-powered infrastructure in large-scale, high-foot-traffic settings. These findings offer a transformative approach to energy sustainability, reducing reliance on traditional energy sources and contributing to Saudi Arabia’s Vision 2030 for renewable energy adoption.
Open Access
Numerical modelling on metallic materials
(MDPI, 2025-04-09) Wen, Shuwen; Sun, Yongle; Chen, Xin
Numerical modelling of metallic materials has emerged as a pivotal research area in modern materials science and engineering [...]
Open Access
Optimization of printing parameters for self-lubricating polymeric materials fabricated via fused deposition modelling
(MDPI, 2025-05-02) Zhang, Peiyang; He, Feiyang; Khan, Muhammad
This study investigated the feasibility of fabricating self-lubrication material using fused deposition modelling (FDM) technology, focusing on the influence of printing parameters on tribological performance. Experiments were conducted using PA and ABS materials, with varying printing speed, infill density, and layer height across four levels. The research established regression equations and fitted curves to describe the relationship between printing parameters and the coefficient of friction (CoF). Validation experiments demonstrated the reliability of the models, with errors within 10%. The results indicate that reducing printing speed and increasing infill density enhance surface quality, with infill density exerting a more significant effect. The influence of layer height on surface quality depends on the printer characteristics, making precise quantification challenging. Additionally, this study confirms that resin-based samples produced via FDM exhibit self-lubricating potential. These findings contribute to the optimization of FDM-printed structures by balancing surface quality and tribological performance.
Open Access
Real-time prediction of wear morphology and coefficient of friction using acoustic signals and deep neural networks in a tribological system
(MDPI, 2025-06-01) Tian, Yang; Zheng, Bohao; Khan, Muhammad; Yang, Yifan
Predicting real-time wear depth distribution and the coefficient of friction (COF) in tribological systems is challenging due to the dynamic and complex nature of surface interactions, particularly influenced by surface roughness. Traditional methods, relying on post-test measurements or oversimplified assumptions, fail to capture this dynamic behavior, limiting their utility for real-time monitoring. To address this, we developed a deep neural network (DNN) model by integrating experimental tribological testing and finite element method (FEM) simulations, using acoustic signals for non-invasive, real-time analysis. Experiments with brass pins (UNS C38500) of varying surface roughness (240, 800, and 1200 grit) sliding against a 304 stainless steel disc provided data to validate the FEM model and train the DNN. The DNN model predicted wear morphology with accuracy comparable to FEM simulations but at a lower computational cost, and the COF with relative errors below 10% compared to experimental measurements. This approach enables real-time monitoring of wear and friction, offering significant benefits for predictive maintenance and operational efficiency in industrial applications.
Open Access
A reliability-oriented framework for the preservation of historical railway assets under regulatory and material uncertainty
(MDPI, 2025-05-01) Wailes, Thomas; Khan, Muhammad; He, Feiyang
Preserving historical railway assets presents a complex systems challenge, in which uncertainties in material performance, structural degradation, and regulatory requirements directly impact long-term reliability and operational continuity. Traditional maintenance practices often limit the use of modern materials, introducing inefficiencies, increased lifecycle costs, and higher failure risk due to material ageing and environmental exposure. This study proposes a reliability-informed preservation framework that supports the integration of contemporary materials into historical railway infrastructure while accounting for legal, material, and procedural uncertainties. The framework is validated through two industrial case studies, each reflecting different regulatory and operational constraints. The first case demonstrates the successful substitution of timber with certified PVC cladding on a non-listed signal box, achieving improved durability, reduced maintenance intervals, and enhanced system reliability. The second case explores an unsuccessful attempt to replace decayed timber gables with aluminium, in which late-stage planning misalignment, underestimated risks, and uncertainty in approval outcomes led to a significant cost increase and reduced reliability regarding delivery. By systematically applying and evaluating the framework under real-world conditions, this research contributes to engineering asset management by introducing a structured method for mitigating regulatory and material uncertainties.
Open Access
Virtual electroencephalogram acquisition: a review on electroencephalogram generative methods
(MDPI, 2025-05-02) You, Zhishui; Guo, Yuzhu; Zhang, Xiulei; Zhao, Yifan
Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward.

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