Search Results (1,330)

Boundary Layer Ingestion propulsors operate in an adverse aerodynamic environment with high levels of distortion. With the purpose of extending the operating range of transonic fan rotors for BLI applications, in this paper we present an optimisation study focused on blade profiles design under different working conditions. Quasi-2D blade sections are optimised using a genetic algorithm and numerical simulations, by varying the camberline and thickness distribution. A method to efficiently achieve a combination of total pressure ratio at a given relative inlet Mach number is devised. The isentropic efficiency is optimised at the design point, concurrently with the stall total pressure ratio at a lower inlet Mach number, in a multi-objective fashion. Pareto-optimal profiles exhibit a moderate leading edge concavity for high efficiency and a straighter fore part with increased trailing edge deflection for higher compression at stall. Optimised airfoils are used in a preliminary three-dimensional evaluation with a realistic BLI inflow, in which the unsteady full-annulus analysis corroborates the approach of the sectional optimisation, also showing the possibility of estimating the integral performance of the machine with a simplified approach based on a single-passage simulation with a circumferential-averaged inflow distribution. Full article

This study addresses the challenge of designing low-carbon supply chain pathways in the global seaborne metallurgical coal sector by developing an enhanced Ant Colony Optimisation (ACO) algorithm. This quantitative approach bridges operations research and sustainability science by identifying optimal supply pathways to minimise transportation-related carbon emissions. The enhanced framework incorporates coal-specific maritime logistical constraints and maintains Pareto efficiency across a comprehensive global dataset encompassing 201 mines, 11 exporting nations, and 72 destination ports in 26 importing countries. Computational analysis demonstrates that the proposed algorithm achieves a 25% reduction in transportation carbon intensity (from 38.2 to 28.6 kg CO₂eq/t) relative to the 2022 baseline. To evaluate supply chain resilience, scenario analyses incorporating geopolitical disruptions, such as the Russian coal sanctions, provide quantitative insights into the trade-offs between policy interventions and emission reduction objectives. Extending projections to 2050 under various demand trajectories yields cumulative emission reductions of 35–70 Mt CO₂eq (an average of 53 Mt), representing additional mitigation beyond the 230 Mt of reductions identified in prior research. These findings demonstrate that mathematical optimisation can deliver near-term environmental benefits without requiring capital-intensive technological breakthroughs, thereby supporting global climate mitigation targets. Full article

Aiming at the problems of a fragile ecological environment, water shortage and system uncertainty in inland arid irrigation districts in Xinjiang, this study takes sustainable development as the guide, selects the Tailan River Irrigation District in Xinjiang as an example, and constructs a multi-objective optimal allocation model of agricultural water and soil resources in irrigation districts driven by water–carbon–economy synergy. The model aims to minimise irrigation water shortage, maximise crop carbon absorption and maximise economic benefits. By comparing six multi-objective algorithms such as APSEA, CMEGL, DCNSGA-III, DRLOS-EMCMO, MOEA/D-CMT and θ-DEA-CPBI, the optimal is selected based on the hypervolume (HV) index. The surface water, groundwater and crop-planting structure of five decision-making units in the irrigation district from 2021 to 2024 were optimised. Further, combined with the entropy weight–TOPSIS coupling-coordination comprehensive-evaluation model, the scheme evaluation system is constructed to screen the optimal configuration scheme of each year and unit. The results show that the MOEA/D-CMT algorithm has the highest HV value in each unit model over the years, which is the best solution algorithm for the model in this paper. The comprehensive evaluation value and coupling coordination degree of the optimal scheme of each unit fluctuate between years, and the difference between units is significant. Compared with the original planting and water source allocation scheme of the irrigation district from 2021 to 2024, the overall planting area of the optimised irrigation district is moderately reduced, forming an optimised pattern of ‘cotton pressure, grain expansion, economic increase and strong forest’; after optimization, the overall water shortage in the irrigation district is reduced by 1.4~11 million m³; the total amount of crop carbon absorption increased by 90.3~128.8 million kg; the net economic benefits increased by CNY 21.5~68.2 million. The research can provide decision support for the optimisation of the water and soil resource system in arid irrigation districts and has a scientific reference value for promoting the sustainable development and modernisation of agriculture in the inland irrigation districts of Northwest China. Full article

A neural network simulation–regression method was developed to assess the impact of sanctions on the level of administrative offences under fragmented, noisy, and short administrative time series. The study addresses the problem of quantifying and predicting changes at the offence level as a sanction size function, using detection probability, prior violation level, compliance costs, and auxiliary contextual factors. The proposed framework combines a hybrid MLP–LSTM neural network, double machine learning-based orthogonal causal estimation, the simulation-based generation of counterfactual scenarios through domain randomization, multiple imputation for missing data, debiasing procedures, and ensemble uncertainty estimation. The contribution to administrative law consists of a quantitative tool creation for substantiating and optimising sanction policy, assessing heterogeneous effects, and supporting evidence-based rulemaking and law enforcement decisions. In comparative experiments, the developed method achieved an RMSE of 8…12%, a prediction accuracy of 93…96%, an overall accuracy of 95%, a precision of 94%, a recall of 93%, and an F1-score of 93.5%, thereby outperforming contemporary econometric, simulation, causal machine learning, and predictive machine learning approaches used for sanction effect modelling. Additional verification through nonparametric statistical testing confirmed that the proposed model’s superiority over the compared algorithms is statistically significant across the main quality metrics, which strengthens the evidence for its robustness and practical value in sanction policy analysis under fragmented administrative data conditions. Full article

Hydropower remains one of the primary sources of power generation. It can be operated as either a base-load or peak-load plant due to its rapid, easy start-up and stop-down capability. However, power plants, old or new, need to be operated and maintained optimally to meet energy demand and maximise economic returns. While the older plants without digital controls such as the Supervisory Control and Data Acquisition (SCADA) system are unable to leverage the evolving technology including big data and Artificial Intelligence (AI), the newer plants or plants that already have some form of data acquisition system have the advantage of leveraging the newer platforms for efficient operation, monitoring and fault diagnosis. Thus, an Artificial Neural Network (ANN), a machine learning (ML) algorithm, was chosen for this case study to predict the generator’s operational stator temperature by selecting six parameters that could potentially affect it. Real data from the 336 MW Chhukha Hydropower Plant (CHP) in Bhutan were used to train the ANN. The prediction of temperature using an ANN in MATLAB^® yielded an R² (correlation coefficient) of 96.8%, which is impressive but can be further improved through various optimisation and tuning methods with increased data volume and complexity. The performance of ANN prediction was validated against other regression models, and the ANN was found to outperform them. This demonstrated its capability to predict and detect generator temperature faults before failures, thereby enhancing hydropower operation and maintenance (O&M) efficiency. The model’s interpretation was also done through Shapley Additive ExPlanations (SHAP). Full article

Construction projects frequently face risks such as anomalies, delays, and bottlenecks, which can substantially affect timelines and budgets. This study proposes a machine learning (ML)-based framework for early identification of risks in construction projects, enabling pattern understanding and decision-making through clustering, outlier and bottleneck detection, and relevant variables identification. It uses a business process management (BPM) dataset of construction documents and applies clustering techniques to both numerical and mixed datasets to group documents with similar characteristics, enabling the detection of temporal deviations and the patterns behind them. Additionally, an ensemble anomaly detection model based on different algorithms is implemented to identify outliers through key variables, which may indicate hidden risks and planning errors. Explainable artificial intelligence (XAI) techniques are then used to analyse the importance of the variables, supporting the identification and analysis of bottlenecks that may compromise project success. The results reveal an F1 score of 0.73 in bottleneck detection using three understandable decision rules, a 6% rate of anomalies within the dataset, and three distinct project clusters. This approach enables accurate and timely detection of risks while providing valuable insights for decision-making, improving risk management, and optimising project execution in the architecture, engineering and construction (AEC) industry. Full article

Most camera view planning algorithms are employed in exploration tasks that maximise information gain, but few address the specific challenge of observing targeted surface areas with optimal image quality. This paper presents a novel camera view planning algorithm designed for dermoscopic mole mapping, which is crucial for early melanoma detection. Traditional full-body scanners, though beneficial, suffer from fixed camera positions that can compromise image quality due to varying body contours and patient sizes. Our algorithm addresses this limitation by dynamically optimizing the camera position on a set of collaborative robot (cobot) arms to enhance image resolution, safety, and viewing angles during skin examinations. The proposed method formulates the problem as a non-linear least-squares optimisation that ensures no camera occlusion and a safe distance from the end effector encapsulating the camera to the patient while adjusting the pose of the camera based on the topography of the body. This approach not only maintains optimal imaging conditions by considering resolution and angle of incidence but also prioritises patient safety by preventing physical contact between the camera and the patient. Extensive testing demonstrates that our algorithm adapts effectively to different body shapes and sizes, ensuring high-resolution images across various patient demographics. Moreover, the integration of our camera view planning algorithm into an intelligent dermoscopy system has shown promising results in improving the efficiency and geometric quality of dermoscopic image acquisition, which could lead to more reliable and faster diagnoses. This technology holds significant potential to transform melanoma screening and diagnosis, providing a scalable, safer, and more precise approach to dermatological imaging. Full article

This paper presents an integrated mathematical model to improve the safety and operational efficiency of train traffic in centralised railway dispatching systems. The proposed approach combines the alternative graph model with a Mealy automaton to synchronously address route planning, delay minimisation, and strict compliance with safety requirements. Formal control theory based on finite-state automata is employed to describe routing logic and signal control through state transitions, while the alternative graph model represents scheduling constraints and resource conflicts. To enhance real-time adaptability, a tabu search algorithm is implemented for train schedule optimisation, enabling dynamic rescheduling under changing operational conditions. The mathematical formulation incorporates blocking time parameters, a system of discrete constraints, and automaton-based safety conditions governing train movements and route authorisation. The integrated model explicitly formalises the processes of block section occupation and release, ensuring consistency between control logic and scheduling decisions. Practical testing and computational experiments demonstrate that the proposed approach effectively reduces train delays, improves the reliability of dispatch control, and increases system resilience to dynamic disturbances. The results confirm that the developed model can be implemented within existing centralised dispatching infrastructures without requiring a complete system overhaul. Overall, the proposed framework expands the functional capabilities of centralised dispatch systems by enabling efficient schedule generation, minimising the propagation of delays, and ensuring reliable command exchange between central control posts and field-level railway infrastructure. Full article

Manufacturing is going through a significant shift propelled by Industry 4.0 and smart manufacturing infrastructures, requiring sophisticated production control techniques that can adaptively adjust to fluctuating operational situations. This paper presents a novel five-step hybrid simulation framework for adaptive real-time production speed control in smart manufacturing lines, integrating conceptual modelling, hybrid simulation, algorithm redefinition, design of experiments, optimisation, and real-system implementation. The framework transforms the speed management systems into online digital twins capable of optimising system performance and mitigating unforeseen fluctuations, faults, and congestion. A comprehensive case study from the beverage manufacturing sector demonstrates the framework’s effectiveness, utilising a universal simulation platform to model both continuous fluid flow and discrete event processes. The proposed stepwise, multi-threshold algorithm employs multiple distinct logical thresholds evaluated sequentially to optimise both upstream and downstream station speeds, with decision thresholds independently adjustable for each production line segment. The experimental results show significant improvements, including around an 18% increase in overall throughput and a 95.7% reduction in work-in-process inventory. A comprehensive resiliency analysis and statistical tests under various disruption scenarios further validated the approach, demonstrating its superiority. Beyond the studied case, the framework provides a transferable pathway for real-time adaptive control across a wide range of smart manufacturing environments, enabling enhancements to operational efficiency without requiring additional capital investment in new equipment or infrastructure. Full article

Enhancing building energy performance has become an essential goal, particularly as building energy management systems (BEMSs) increasingly rely on high-quality data and reliable predictive models. Although machine learning (ML) models have been widely applied to building energy prediction, optimisation, and management, their reliability in practice is often constrained by data preprocessing rather than algorithm selection. Existing studies often emphasise algorithmic development while providing limited systematic investigation of preprocessing practices, leading to methodological misconceptions and reduced robustness of ML-driven building energy management. As a novel contribution, this article presents a systematic review of 73 scientific articles published from 2020 to 2025 in the field of preprocessing practices. To this goal, a three-step data preprocessing workflow is organised, comprising data analysis, data preparation, and feature engineering. The strengths, limitations, and recurring misconceptions of preprocessing techniques adopted in the analysed studies are synthesised, with emphasis on their impact on prediction accuracy, interpretability, and model robustness. As a result, this review reframes the data preprocessing stage as a decision-making process in which data analysis and the energy improvement task constrain and inform subsequent data preparation and feature engineering steps to address building energy performance enhancement tasks. Full article

This paper presents a design research study that develops a comprehensive conceptual framework for an integrated digital twin system for architectural heritage. The framework aims to explore mechanisms for real-time monitoring and the coupled regulation of structural health, microclimatic conditions, and energy performance. In the context of the ongoing global warming emergency, this framework supports climate adaptation strategies for heritage sites. It enables a fully coordinated operational process encompassing real-time sensing, predictive analysis, coupled control, and decision support. In the structural dimension, the framework is designed to utilise sensors to monitor and warn against cracks, settlement, and deformation, whilst integrating models to analyse stress conditions. In the microclimate dimension, the study envisages predicting and adjusting HVAC and lighting systems based on environmental parameters and footfall monitoring data via algorithms, with the aim of balancing occupant comfort with humidity control and mould prevention. Regarding energy, the framework optimises equipment operation through smart metering and algorithms and we propose a modelling tool for the quantitative assessment of energy-saving retrofit effects. Furthermore, the framework incorporates the establishment of an open-access dataset covering structural, microclimate, and energy use data, providing data standards and a foundation for subsequent empirical research. Full article

Precise orbital floor reconstruction requires personalised surgical templates that combine high geometric fidelity with manufacturing efficiency. This study presents and validates the TARMM procedure, developed to optimise the production of polylactide (PLA) templates. A key innovation is the integration of advanced machine learning algorithms (Random Forest) and Mitchell–Netravali interpolation to reduce medical reconstruction artefacts, as well as the implementation of Material Extrusion (MEX) technology with Variable Layer Height (VLH). This strategy minimises the stair-step effect on complex anatomical curvatures while maintaining high process throughput. The results demonstrate that the TARMM procedure ensures a geometric error within ±0.1 mm. A strong linear correlation (r = 0.99) was found between layer height and surface roughness (Sa), indicating that a 0.07 mm layer in critical areas significantly improves template morphology and facilitates the contouring of titanium meshes. The clinical validation across 21 cases confirmed a 30 min reduction in surgical preparation time. The developed method serves as a low-cost, high-precision alternative to photopolymerization technologies, contributing to modern 3D printing applications in maxillofacial surgery. Full article

This paper argues that digital identity in AI-mediated environments has become a central mechanism through which contemporary societies organise recognition, participation, and belonging. Digital identity is no longer simply a technical representation of the individual. It is produced through infrastructural processes of classification, ranking, and credibility signalling that determine who becomes visible, who is treated as legitimate, and who is able to participate meaningfully in social and civic life. The paper develops a conceptual framework that treats AI-driven platforms as social infrastructures rather than neutral intermediaries. It shows how identity is inferred through data-driven systems rather than negotiated through social interaction, how recognition is operationalised through visibility and credibility metrics rather than ethical judgement, and how participation becomes conditional on algorithmic allocation of attention rather than guaranteed by access alone. Visibility is identified as the key conversion point through which inferred identity becomes social consequence. Drawing on interdisciplinary literature, the analysis demonstrates that misrecognition, exclusion, and inequality in platform environments are not primarily the result of isolated error or intentional bias. They are patterned outcomes of ordinary optimisation processes that distribute legitimacy and opportunity unevenly across social groups. These dynamics reshape group formation, harden social boundaries, and concentrate risk among populations that are already more vulnerable to misrecognition and reduced contestability. The paper concludes that governing digital identity is a societal challenge rather than a purely technical one. As platforms increasingly perform institutional functions without equivalent accountability, digital identity governance becomes a critical site of social ordering. Addressing this challenge requires public standards for how visibility, recognition, and participation are allocated, meaningful avenues for contestation, and protections against the normalisation of stratified belonging in AI-mediated societies. Full article

A comprehensive analysis of three graph-theoretic path planning algorithms designed for multi-robotic systems (MRS) was undertaken. The algorithms were the multi-robot path planning algorithm (MRPP), central algorithm (CA), and the optimisation central algorithm (OCA). The primary objective of these algorithms is to enhance path optimality, mitigate computational complexity, and ensure robust inter-robot collision avoidance. The MRPP is a composite approach integrating the visibility graph (VG) for path generation. The CA, derived from VG principles, utilises a central baseline (CB) approach to reduce vertex count, thereby decreasing computational cost while maintaining path efficiency. The OCA extends CA by integrating obstacle expansion and safety margins to enhance collision avoidance and path optimisation. Comparative analysis through simulations in 2D polygonal environments compared the performance of these algorithms, considering their computational efficiency, path optimisation, and collision avoidance. CA and OCA demonstrated significant improvement over the VG-based approach, especially concerning optimality and optimisation. CA reduced the average path length by 4.3% compared with MRPP, while OCA achieved a 6.8% reduction over MRPP, and 2.5% over CA, demonstrating its superior balance between optimality and efficiency. MRPP offers robust connectivity, making it preferable in scenarios where communication is critical. The study’s findings assist in devising MPRPP solutions. Full article

Approximately one quarter of the European Union’s (EU’s) CO₂ emissions originate from the transport sector, of which road transport, such as cars and heavy-duty vehicles, contributes roughly 72%. Moreover, according to the European Automobile Manufacturers’ Association, 92% of cars in the EU are internal combustion engine vehicles powered by fossil fuels. Therefore, boosting the adoption of Electric Vehicles (EVs) is considered one of the most prominent solutions for reducing GHG emissions and achieving the EU’s climate targets. To increase EV adoption and fulfil the demand of EV users, adequate EV Charging Stations (EVCSs) are required. Nevertheless, since most EVCSs are supplied by electricity grids that remain predominantly fossil fuel-based, their operation entails substantial indirect GHG emissions. A prominent approach to reducing grid-related emissions is integrating renewable energy sources (RESs) with EVCSs, thereby lowering emissions and alleviating grid stress. Although promising, the energy, economic, and environmental (3E) benefits of this integration remain insufficiently explored. Therefore, this study develops and applies a 3E optimisation framework to assess the feasibility and performance of RES-powered EVCS at NOVA University Lisbon (UNL). Data was collected from the UNL parking area, such as time of arrival, and time of departure. Also, a rule-based algorithm was developed to curate data and estimate the EVCS load profile. Furthermore, HOMER optimisation software was employed to evaluate four scenarios, including (i) an EVCS based on PV, Wind Turbine (WT), and the grid, (ii) an EVCS based on PV and the grid, (iii) an EVCS based on WT and the grid, and (iv) an EVCS based only on energy withdrawal from the grid (base scenario). Under the adopted techno-economic assumptions, in the most optimised scenario, economic and environmental analyses illustrate significant improvements over the base scenario: CO₂ emissions are five times lower, and cost of energy is significantly lower, resulting in significantly lower EV charging costs for users. The results demonstrate that, through developed feasibility studies, researchers, decision-makers, and stakeholders can reach better conclusions about EVCS planning and management. Full article