Search Results (1,345)

Equipment renewal and replacement strategy as well as smart capital investment is a vital focus in engineering asset management, particularly for water utilities aiming to improve asset reliability, water quality, service continuity and affordability. This study presents a novel decision support model that integrates whole-life costing principles across all asset lifecycle phases—from capital delivery and daily operations to long-term maintenance. The proposed model uniquely combines asset degradation and failure patterns, operating and maintenance costs, and the impact of technological advancements to provide a holistic and comprehensive asset management decision-making tool. These dimensions are jointly analysed using a hybrid approach that combines optimisation with stochastic dynamic programming, allowing for the determination of optimal asset renewal and replacement timing. The model’s performance was validated using historical data from eight critical wastewater pump stations within a township’s sewerage network. This was performed by comparing the model’s cost-saving results to those achieved by the water utility’s current strategy. Results revealed that the proposed model achieved an average cost saving of 12%, demonstrating its effectiveness in supporting sustainable and cost-efficient asset renewal decisions. Full article

The subject of this paper is how certain electrode array parameters affect the operating characteristics of electrohydrodynamic (EHD) propulsion systems. The focus is on how changes in the shapes and arrangements of electrodes, such as the diameter of the coronating conductor, effective electrode length and the spacing between electrodes, influence the formation and behaviour of the corona discharge and the resulting ion-induced airflow. A modular experimental setup was created to allow for a systematic study of each parameter in controlled atmospheric conditions using a high-voltage DC power supply. The study includes both the theoretical background and experimental methods, in order to explore the connections between the electric field distribution, ion mobility and propulsion force generation. By measuring the current, voltage and flow velocity, the impacts of design changes on the propulsion behaviour are examined. The findings help to improve the understanding of EHD propulsion mechanics and lay the groundwork for optimising electrode designs in future applications. This research supports the ongoing work to create compact, quiet and efficient propulsion technologies for use in lightweight aerial vehicles, precise fluid control and other engineering areas, where solid-state thrust systems have clear benefits over traditional methods. Full article

This paper presents a detailed numerical investigation of the effect of hub-mounted riblets on the thermal and aerodynamic performance of a radial turbine rotor. While prior studies have shown that riblets reduce wall shear stress and improve aerodynamic efficiency, their influence on heat transfer and thermal losses remains underexplored. Using numerical simulations, this study examines the heat transfer characteristics within the rotor passage, comparing ribbed and smooth hub configurations under the same operating conditions. Results reveal that although riblets reduce frictional drag, they also enhance convective heat transfer—leading to a 6% increase in the heat transfer coefficient at the hub and 2.8% at the blade surfaces. This intensification of heat transfer results in a 4.3% rise in overall thermal losses, counteracting some of the aerodynamic gains. The findings provide new insights into the thermofluidic implications of surface modifications in turbomachinery and emphasise the importance of considering surface finish not only for aerodynamic optimisation but also for thermal efficiency. These results can inform future turbine design and manufacturing practices aimed at controlling surface roughness to minimise heat loss. Full article

Background: The purpose of alveolar ridge preservation (ARP) is to minimise the physiological alveolar ridge reduction occurring after dental extraction, which can prevent the need for future alveolar ridge augmentation. Biologic materials (biologics) promote tissue regeneration based on their effect on wound healing at a cellular level. By integrating biologics into ARP biomaterials, there is a potential to enhance the regeneration of both hard and soft tissues with greater efficacy. Aim: This narrative review aims to evaluate the clinical efficacy of the addition of biologics to existing ARP materials on the physiological changes following ARP of an extraction site. Methods: A search of the PubMed electronic database was conducted, and relevant articles were examined. Sixty-three articles met the inclusion and exclusion criteria and were included in this review. Results and Conclusions: A review of the existing literature found that the combination of biologics with ARP materials resulted in similar dimensional changes when compared to using ARP materials alone. Existing research has identified an enhancement in bone density, increased wound healing capacity of soft and hard tissue, and a reduction in post-operative pain. Whilst the addition of biologics to ARP materials has shown an increase in bone density, its effectiveness in improving implant outcomes and reducing the need for future alveolar ridge augmentation is unclear. Recognising the limitations within the existing literature, along with the risk of bias and heterogeneity, renders it unwise to make definite conclusions about the benefits of integrating biologics with ARP materials. This narrative review found possible benefits in the use of biologics in ARP to optimise patient-related and treatment outcomes, indicating the need for additional research. Full article

The utilisation of agricultural residues for power generation is an opportunity to reduce fossil fuel usage and foster a sustainable circular economy in Mediterranean European regions. This can be achieved by resorting to the gasification process, which faces challenges such as optimising its operation parameters on real-world applications and lowering operational costs. This work studies the gasification process of a set of agricultural biomasses widely available in the Mediterranean Europe regions through modelling and simulation in Aspen Plus. The selected biomasses are olive stone, grapevine waste, and wheat straw. The effect of temperature, equivalence ratio, and steam-to-biomass ratio on gasifier performance and their effect on gas composition was assessed. The results indicate that olive stone and wheat straw performed best in terms of syngas composition and cold gas efficiency. The analyses show good gasification performance for temperatures above 750 °C, equivalence ratios ranging from 0.1 to 0.3, depending on the raw material and steam-to-biomass ratios below 0.1. The obtained values show the validity and the potential of a downdraft gasification reactor to be used with these abundant agricultural biomasses in the Mediterranean European region. Its integration with a reciprocating engine is a rational choice for distributed power generation. Full article

The operational roles of artificial intelligence in energy security remain inconsistently defined across the scientific literature. To address this gap, the present review examines 165 peer-reviewed abstracts published between 2021 and 2025 using a triangulated methodology that combines trigram frequency analysis, manual qualitative coding, and semantic clustering with sentence embeddings. Eight core roles were identified: forecasting and prediction, optimisation of energy systems, renewable energy integration, monitoring and anomaly detection, grid management and stability, energy market operations/trading, cybersecurity, and infrastructure and resource planning. According to the results, the most frequently identified roles, based on the average distribution across all three methods, are forecasting and prediction, optimisation of energy systems, and energy market operations/trading. Roles such as cybersecurity and infrastructure and resource planning appear less frequently and are primarily detected through manual interpretation and semantic clustering. Trigram analysis alone failed to capture these functions due to terminological ambiguity or diffuse expression. However, correlation coefficients indicate high concordance between manual and semantic methods (Spearman’s ρ = 0.91), confirming the robustness of the classification. A structured typology of AI roles supports the development of more coherent analytical frameworks in energy research. Future research incorporating full texts, policy taxonomies, and real-world use cases may help integrate AI more effectively into energy security planning and decision support environments. Full article

Background: The growing demand for fresh food leads to extensive use of cold chain logistics (CCL) that significantly contributes to greenhouse gas (GHG) emissions due to its dependence on energy-intensive transport refrigeration units (TRUs). Understanding the need to balance food preservation with environmental sustainability, this paper explores practical strategies for reducing GHG emissions in CCL, focusing on fresh food products. Methods: The quantitative and qualitative analyses are applied to analyse data from Transport for London and Transport Scotland. Emission data were assessed to evaluate the impact of alternative TRU technologies and route optimisation practices. Results: The findings reveal that electric and cryogenic TRUs, along with improved route planning and operational practices, can significantly reduce the emissions of carbon dioxide, nitrogen oxides and particulate matter. These results highlight the potential strategy for industry-led emission reductions without compromising food quality. Conclusions: This paper recommends the coordination of government policy and industry to support technological adaptation and infrastructure upgrades and to research into real-time monitoring and renewable energy integration in CCL systems. Full article

Harnessing the synergistic potential of graphene oxide-doped titanium dioxide (GO-TiO₂), this study pioneers an advanced photocatalytic approach by incorporating graphene oxide-doped titanium dioxide (GO-TiO₂) as a catalyst to enhance the photocatalytic degradation of levofloxacin (LVX), with optimisation of parameters using response surface methodology (RSM) and artificial neural networks (ANNs). By adjusting key operational parameters such as catalyst dosage, LVX concentration, pH, and percentage dopant in TiO₂, the study aimed to maximise degradation efficiency. The RSM statistical model highlighted optimal conditions, i.e., neutral pH, 0.1 g/g dopant, 1.1 g/L catalyst, and 25 ppm LVX concentration, achieving a degradation efficiency close to 80% (R² = 0.88). An ANN model was also developed, offering a three-layer neural network that accurately predicts LVX degradation under varied conditions, with R² reaching 0.97. Current modelling techniques frequently fail to strike a balance between practical insights for optimising photocatalytic degradation and predictive accuracy. By combining the parametric insights of RSM with the nonlinear predictive power of ANN, this study closes that gap and develops a sustainable, data-driven framework for effectively breaking down pharmaceutical pollutants and developing environmentally friendly wastewater treatment methods. Full article

Background/objectives: Robotic-assisted partial nephrectomy (RAPN) is increasingly utilised for the management of renal masses, with the growing use of different robotic platforms and increasing complexity of renal masses managed robotically. Appropriate patient selection, the development of operative skills and experience and sensible surgical decision making are required to optimise the outcomes of RAPN and minimise the risk of complications. We provide a comprehensive review of strategies to predict, avoid and manage the complications of RAPN. Methods: We conducted a comprehensive literature review to outline many of the reported complications arising from RAPN, with a focus on preoperative considerations (patient selection, imaging, 3D modelling and predictive models), intraoperative considerations (positioning and kidney exposure complications) and practical management strategies to identify and manage the complications of this procedure. Results: Many complications of RAPN can be predicted, and we outline strategies to mitigate these risks through careful preparation prior to surgery, including descriptions of preventative strategies and important preoperative considerations. We also present a detailed outline of management for the most common complications of RAPN, including bleeding/haemorrhage, urine leak and intraoperative complications such as adjacent organ injuries. Conclusions: RAPN can be a challenging procedure with a significant risk of complications. Assiduous preoperative planning, thoughtful intraoperative decision making and the early recognition and management of complications are essential to optimise patient outcomes following RAPN. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $\mathrm{49\%}$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

The aim of this work is to analyse the effectiveness of the medical use of the Cavitron Ultrasonic Surgical Aspirator (CUSA) in microsurgical treatment of Intramedullary Spinal Cord Tumors (IMSCTs), with a focus on the thermo-mechanical effects on neighbouring tissues to assess any potential damage. Indeed, CUSA emerges as an innovative solution, minimally invasive tumor excision technique, enabling controlled and focused operations. This study employs a Finite Element Analysis (FEA) to simulate the vibratory and thermal interactions occurring during CUSA application. A computational model of a vertebral column segment affected by an IMSCT was developed and analysed using ANSYS 2024 software. The simulations examined strain distribution, heat generation, and temperature propagation within the biological tissues. The FEA results demonstrate that the vibratory-induced strain remains highly localised to the application site, and thermal effects, though measurable, do not exceed the critical safety threshold of 46 °C established in the literature. These findings suggest that CUSA can be safely used within defined operational parameters, provided that energy settings and exposure times are carefully managed to mitigate excessive thermal accumulation. These conclusions contribute to the understanding of the thermo-mechanical interactions in ultrasonic tumour resection and aim to assist medical professionals in optimising surgical protocols. Full article

Though the pursuit of sustainable desalination processes with high water recovery has intensified the research interest in membrane distillation (MD), the influence of module connection configuration on performance stability remains poorly explored. The current study provided a comprehensive multiparameter assessment of hollow fibre membrane modules connected in parallel and series in direct contact membrane distillation (DCMD) for the first time. The configurations were evaluated under varying process parameters such as temperature (50–70 °C), flow rates (22.1–32.3 mL·s⁻¹), magnesium concentration as scalant (1.0–4.0 g·L⁻¹), and flow direction (co-current and counter-current), assessing their influence on temperature gradients (∆T), flux and pH stability, salt rejection, and crystallisation. Interestingly, the parallel module configuration maintained high operational stability with uniform flux and temperature differences (∆T) even at high recovery factors (>75%). On one hand, the serial configuration experienced fluctuating ∆T caused by thermal and concentration polarisation, causing an early crystallisation (abrupt drop in feed conductivity). Intensified polarisation effects with accelerated crystallisation increased the membrane risk of wetting, particularly at high recovery factors. Despite these changes, the salt rejection remained relatively high (99.9%) for both configurations across all tested conditions. The findings revealed that acidification trends caused by MgSO₄ were configuration-dependent, where the parallel setup-controlled rate of pH collapse. This study presented a novel framework connecting membrane module architecture to mass and heat transfer phenomena, providing a transformative DCMD module configuration design in water desalination. These findings not only provide the critical knowledge gaps in DCMD module configurations but also inform optimisation of MD water desalination to achieve high recovery and stable operation conditions under realistic brine composition. Full article

The response time of Artificial Neural Network (ANN) inference is critical in embedded systems processing sensor data close to the source. This is particularly important in applications such as predictive maintenance, which rely on timely state change predictions. This study enables estimation of model response times based on the underlying platform, highlighting the importance of benchmarking generic ANN applications on edge devices. We analyze the impact of network parameters, activation functions, and single- versus multi-threading on response times. Additionally, potential hardware-related influences, such as clock rate variances, are discussed. The results underline the complexity of task partitioning and scheduling strategies, stressing the need for precise parameter coordination to optimise performance across platforms. This study shows that cutting-edge frameworks do not necessarily perform the required operations automatically for all configurations, which may negatively impact performance. This paper further investigates the influence of network structure on model calibration, quantified using the Expected Calibration Error (ECE), and the limits of potential optimisation opportunities. It also examines the effects of model conversion to Tensorflow Lite (TFLite), highlighting the necessity of considering both performance and calibration when deploying models on embedded systems. Full article