Search Results (3,180)

This work presents the design, the development and the experimental validation of a portable, low-cost sensing system for the detection of waterborne pollutants. The proposed system is based on Electrochemical Impedance Spectroscopy and PPF+Ni nanomembrane sensors. Designed in response to the increasing demand for in situ water quality monitoring, the system integrates a simplified, scalable EIS acquisition architecture compatible with microcontroller-based platforms. The sensing configuration utilises the voltage divider principle, ensuring simplicity in signal conditioning by allowing compatibility with different electrode types through passive impedance matching. In addition, new merit figures have been proposed and implemented to analyse the measures. The proposed platform was experimentally characterised for its measurement stability, accuracy and environmental robustness. Sensitivity tests using benzoquinone as a target analyte demonstrated the capability of detecting concentrations as low as 0.1 mM with a monotonic response over increasing concentrations. A comparative study with a commercial electrochemical system (PalmSens4) under identical conditions highlighted the higher resolution and practical advantages of the proposed method despite operating with a lower impedance range. Additionally, the system exhibited reliable discrimination across tested concentrations and greater adaptability for integration into field-deployable environmental monitoring platforms. Future developments will focus on optimising selectivity through new sensor materials and analytical modelling of uncertainty propagation in the analysis based on defined figures of merit. Full article

Agriculture stands at a pivotal juncture in the twenty-first century, confronting the converging crises of climate change, biodiversity loss and rising food demand, even as it is increasingly recognised as part of the solution. This paper assesses the transformative potential of integrating three emerging paradigms—digital agriculture, regenerative agriculture and decommoditised agriculture—into a unified approach capable of delivering productivity, ecological restoration and economic viability. Digital agriculture deploys artificial intelligence, Internet of Things (IoT) networks and remote sensing to optimise inputs and sharpen decision-making. Regenerative agriculture seeks to rebuild soil function, enhance biodiversity and restore ecosystem processes through holistic, adaptive management. Decommoditised agriculture reorients value chains from bulk markets towards quality-differentiated systems that privilege direct producer–consumer relationships, value-added processing and regional market development, enabling price premiums and community resilience. We examine their convergence through the “3N” lens—net-zero greenhouse gas emissions, nature-positive outcomes and nutrition-balanced food systems. Integration creates clear complementarities: digital tools monitor, verify and optimise regenerative practices; regenerative systems provide the ecological foundation for sustainable intensification; and decommoditised models supply economic incentives that reward stewardship and nutritional quality. Persistent barriers include the digital divide, data governance, technical complexity and fragmented policy settings. Realising the benefits will require technology democratisation, interdisciplinary research, enabling regulation and farmer-centred innovation processes. We conclude that converging digital, regenerative and decommoditised approaches offers a credible and necessary pathway to resilient, sustainable and equitable agri-food systems. Full article

Electromagnetic (EM) metamaterials have a wide range of applications due to their unique properties, but their design is often based on specific topological structures, which come with certain limitations. Designing with stochastic topologies can provide more diverse EM properties. However, this requires experienced designers to search and optimise in a vast design space, which is time-consuming and requires substantial computational resources. In this paper, we employ a deep learning network agent model to replace time-consuming full-wave simulations and quickly establish the mapping relationship between the metamaterial structure and its electromagnetic response. The proposed framework integrates a Convolutional Block Attention Module-enhanced Variational Autoencoder (CBAM-VAE) with a Transformer-based predictor. Incorporating CBAM into the VAE architecture significantly enhances the model’s capacity to extract and reconstruct critical structural features of metamaterials. The Transformer predictor utilises an encoder-only configuration that leverages the sequential data characteristics, enabling accurate prediction of electromagnetic responses from latent variables while significantly enhancing computational efficiency. The dataset is randomly generated based on the filling rate of unit cells, requiring only a small fraction of samples compared to the full design space for training. We employ the trained model for the inverse design of metamaterials, enabling the rapid generation of two cells for 1-bit coding metamaterials. Compared to a similarly sized metallic plate, the designed coding metamaterial radar cross-section (RCS) reduces by over 10 dB from 6 to 18 GHz. Simulation and experimental measurement results validate the reliability of this design approach, providing a novel perspective for the design of EM metamaterials. Full article

Postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. The early identification of bleeding risk in individual women is crucial for enabling timely interventions and improving patient outcomes.This study aims to evaluate various exploratory and classification methodologies, alongside optimisation strategies, for identifying predictors of postpartum haemorrhage. K-means clustering was employed on a retrospective cohort of patients, incorporating demographic, obstetric, and laboratory variables, to delineate patient profiles and select pertinent features. Initially, a classical logistic regression model, implemented without cross-validation, facilitated the identification of six significant predictors for postpartum haemorrhage: lactate dehydrogenase, urea, platelet count, non-O blood group, gestational age, and first-degree lacerations, all of which are variables routinely available in clinical practice. Furthermore, machine learning algorithms—including stepwise logistic regression, ridge logistic regression, and random forest—were utilised, applying cross-validation to optimise predictive performance and enhance generalisability. Among these methodologies, ridge logistic regression emerged as the most effective model, achieving the following metrics: sensitivity 0.857, specificity 0.875, accuracy 0.871, F1-score 0.759, and AUC 0.907. While machine learning techniques demonstrated superior performance, the integration of classical statistical methods with machine learning approaches provides a robust framework for generating reliable predictions and fostering significant clinical insights. Full article

Turbulence and wind gusts pose significant risks to the safety and efficiency of UAVs (uncrewed aerial vehicles) in urban environments. In these settings, wind dynamics are strongly influenced by interactions with buildings and terrain, giving rise to small-scale phenomena such as vortex shedding and gusts. These wind speed oscillations generate unsteady forces that can destabilise UAV flight, particularly for small vehicles. Additionally, predicting their formation requires high-resolution Computational Fluid Dynamics (CFD) models, as current weather forecasting tools lack the resolution to capture these phenomena. However, such models require 3D representations of study areas with high geometric consistency and detail, which are not available for most cities. To address this issue, this work introduces an automated methodology for urban CFD mesh generation using open-source data. The proposed method generates error-free meshes compatible with OpenFOAM and includes tools for geometry modification, enhancing solver convergence and enabling adjustments to mesh complexity based on computational resources. Using this approach, CFD simulations are conducted for the city of Ourense, followed by an analysis of their impact on UAV operations and the integration of the system into a trajectory optimisation framework. The CFD model is also validated using experimental anemometer measurements. Full article

This paper proposes a household energy management system for all-electric households, focusing on the interplay between cost savings and occupant comfort through an implicit demand response programme. A sequential multi-objective optimisation model is developed based on the lexicographic approach, allowing for the effective prioritisation of objectives. The model optimally schedules a diverse range of electricity demands using real-world data from a Norwegian pilot household to evaluate its unique flexibility potential, while remaining adaptable for other regions. This includes integrating thermal and non-thermal demands with electric mobility via vehicle-to-home enabled electric vehicle charger. This approach achieves significant cost savings on energy bills and enhances user comfort across aggregated comfort indicators. Multiple scenarios are designed to evaluate the performance of the proposed demand response under diverse pricing mechanisms. Results indicate that transitioning from variable pricing to fixed pricing can lead to lower average electricity costs and higher average user comfort. The analysis reveals that prioritising occupant comfort can substantially increase electricity demand, resulting in a nearly fourfold rise in average annual expenses, while also leading to a decrease in self-consumption and self-sufficiency. Additionally, the study illustrates how grid tariff adjustments can benefit households and support the development of local renewable energy. Full article

This study presents a three-dimensional finite element (FE) analysis of the human foot bone structure under mid-stance loading during race walking. A subject-specific biomodel comprising 26 bones and over 40 ligaments was reconstructed from computed tomography (CT) data using Materialise Mimics Research 21.0 and 3-Matic Research 13.0, and subsequently analyzed in ANSYS Workbench 2024 R1. The model included explicit cortical, trabecular, and ligamentous volumes, each assigned linear-elastic, isotropic material properties based on biomechanical literature data. Boundary conditions simulated the mid-stance phase of race walking, applying a distributed plantar pressure of 0.25 MPa over the metatarsal and phalangeal regions. Numerical simulations yielded maximum total displacements of 0.00018 mm, maximum von Mises stresses of 0.171 MPa, and maximum strains of 2.5 × 10⁻⁵, all remaining well within the elastic range of bone tissue. The results confirm the model’s numerical stability, geometric fidelity, and capacity to represent physiologically realistic loading responses. The developed framework demonstrates the potential of high-resolution, image-based finite element modelling for investigating stress–strain patterns of the foot during athletic gait, and establishes a reproducible reference for future analyses involving pathological gait, orthotic optimisation, and musculoskeletal load assessment in sports biomechanics. Full article

Understanding the thermal environment of outdoor public spaces is critical for climate-responsive architectural design, evidence-based urban science, and data-driven smart city planning. Thermal comfort shapes both individual decision-making and collective behavioural patterns, offering valuable insights for designing spaces that support year-round vitality. This study investigates the relationship between thermal conditions and crowd behaviour in severe cold regions by combining behavioural mapping with on-site environmental measurements. Results show that in high-temperature conditions, spatial distribution is primarily influenced by sunlight and shade, whereas at low temperatures, sunlight has minimal effect on space use. Attendance, duration of stay, and activity intensity follow quadratic relationships with the Universal Thermal Climate Index (UTCI), with optimal values at 29 °C, 26 °C, and 27 °C, respectively. Walking speed is inversely correlated with UTCI, with the fastest speeds observed under cold discomfort, reflecting rapid departure from space. Sitting behaviour peaks at 21 °C UTCI and declines to nearly zero when UTCI is below 10 °C. A comparative analysis between Harbin and other regions reveals significant deviations from temperate zone patterns and greater similarity to subtropical behavioural responses. A key contribution of this study is the introduction of the spatial usage rate model and the foot vote method, two novel, observation-based tools that allow for the objective estimation of thermal comfort without relying solely on subjective surveys. These methods offer architects, planners, and smart city practitioners a powerful evidence-based framework to evaluate and optimise outdoor thermal performance, ultimately enhancing usability, adaptability, and public engagement in cold-climate cities. Full article

The study is referenced by interdisciplinary theories, i.e., routine activity, and social cohesion, to investigate the impacts of sport clubs and events on London’s expressive crimes at varied geographical scales, by utilizing Geographical-temporally weighted regression model. It has identified the spatial patterns of effects from sport clubs’ onto local expressive crimes among London wards, with several boroughs standing out for their being significantly affected. The case study in the home borough of the Hotspur Football Club has further been conducted, by proving the seasonal influences of sports clubs on reducing youth violence within school terms. It was also found disproportional increases in expressive crimes on Premier League match days, especially when receiving the results of draw. The data-driven evidence has generated insights on localized policies and strategies on developing tailored sports to support local young people’s development; pinpointing the optimisation of police forces resources on stop and search practices during sports events in hot spot stadiums. The methodology and workflow had also been proved with high replicability into other UK cities. Full article

Offshore vessels are nowadays equipped with dynamic positioning systems, meaning they have additional thrusters dedicated to the station keeping of the unit. However, there is no rational criterion on the placement of these devices to increment station keeping capabilities. This is true both in case of a vessel retrofitting or for the design of a new unit. The present work proposes investigating a methodology for the optimal placement of thrusters along the hull of an offshore unit. This implies the adoption of a suitable optimisation algorithm capable of handling all the constraints of the optimisation problem. As the target is the optimal capability, the optimisation should handle multiple dynamic positioning capability calculations, meaning (in a quasi-static approach) that it is capable of solving multiple thrust allocation problems at each optimisation step. As thruster allocation is another optimisation problem, the process should handle two nested optimisations. Here, the global location problem is solved with a differential evolution algorithm, while the thrust allocation employs non-linear programming. As the capability calculations imply the adoption of a specific wind–wave correlation, the present work compares the effect of different correlations on the optimised location of the thrusters. The results presented on a reference Pipe Lay Crane Vessel highlight the differences in the final optimum as a function of the environmental modelling. Full article

Enhancing productivity while reducing environmental impact presents a major engineering challenge in sustainable dairy farming. This study proposes an engineering-oriented explainable machine learning and digital twin framework for multi-objective optimisation of milk yield and nitrogen-related emissions. Using the CowNflow dataset, which integrates individual-level nitrogen balance, feeding, and production data collected under controlled experimental conditions, the framework combines data analytics, feature selection, predictive modelling, and SHAP-based explainability to support decision-making in dairy production. The stacking ensemble model achieved the best predictive performance ($R^2$ = 0.85 for milk yield and $R^2$ = 0.794 for milk urea), providing reliable surrogates for downstream optimisation. Predicted milk urea values were further transformed using empirical equations to estimate urinary urea nitrogen (UUN) and ammonia (NH₃) emissions, offering an indirect yet practical approach to assess environmental sustainability. Furthermore, the predictive models are integrated into a digital twin platform that provides a dynamic, real-time simulation environment for scenario testing, continuous optimisation, and data-driven decision support, effectively bridging data analytics with sustainable dairy system management. This research demonstrates how explainable AI, machine learning, and digital twin engineering can jointly drive sustainable dairy production, offering actionable insights for improving productivity while minimising environmental impact. Full article

Fully grouted rock bolts play a vital role in stabilising underground excavations, particularly in corrosive environments where material properties, geometric configuration, and installation conditions influence their load transfer performance. Although the practical importance of fully grouted fibreglass rock bolts is well recognised, quantitative evidence on their axial load transfer mechanisms remains limited. Prior work has primarily centred on steel rock bolts, with few studies on how embedment length, grout stiffness, interface roughness and confining stress govern bond mobilisation in fully grouted fibreglass rock bolts, indicating a clear need for further scientific investigation. This study examines the axial load transfer and shear behaviour of fully grouted fibreglass rock bolts, focusing on the effects of embedment length (EL), grout properties, and boundary conditions. A comprehensive series of laboratory pull-out tests were conducted on two widely used Australian glass fibre reinforced polymer (GFRP) rock bolts, TD22 and TD25, with diameters of 22 mm and 25 mm, respectively, under varying ELs and grout curing times to evaluate their axial performance. Additionally, single shear tests and uniaxial compressive strength (UCS) tests were conducted to assess the shear behaviour of the rock bolts and the mechanical properties of the grout. The results showed that increased EL, bolt diameter, and grout curing time generally enhance axial capacity. With grout curing from day 7 to the day 28, the influence of embedment length became increasingly pronounced, as the axial peak load rose from 35 kN (TD22-50, 7 days) to 116 kN (TD22-150, 28 days) and from 39 kN (TD25-50, 7 days) to 115 kN (TD25-150, 28 days), confirming that both longer bonded lengths and extended curing significantly enhance the axial load-bearing capacity of fully grouted GFRP rock bolts. However, the TD22 rock bolts exhibited superior shear strength and ductility compared to the TD25 rock bolts. Also, a calibrated distinct element model (DEM) was developed in 3DEC to simulate axial load transfer mechanisms and validated against experimental results. Parametric studies revealed that increasing the grout stiffness from 5 e7 N/m to 5 e8 N/m increased the peak load from 45 kN to 205 kN (approximately 350%), while reducing the peak displacement, indicating a shift toward a more brittle response. Similarly, increasing the grout-bolt interface roughness boosted the peak load by 150% (from 60 kN to 150 kN) and enhanced residual stability, raising the residual load from 12 kN to 93.5 kN. In contrast, confining stress (up to 5 MPa) did not affect the 110 kN peak load but reduced the residual load by up to 60% in isotropic conditions. These quantitative findings provide critical insights into the performance of GFRP bolts and support their optimised design for underground reinforcement applications. Full article

With the ever-growing reliance on cloud computing, efficient resource allocation is crucial for maximising the effective use of provisioned resources from cloud service providers. Proactive resource management is therefore critical for minimising costs and striving for net zero emission goals. One of the most promising methods involves the use of Artificial Intelligence (AI) techniques to analyse and predict resource demand, such as cloud CPU utilisation. This paper presents MambaNet0, a Mamba-based cloud resource prediction framework. The model is implemented on Google’s Vertex AI workbench and uses the real-world Bitbrains Grid Workload Archive-T-12 dataset, which contains the resource usage metrics of 1750 virtual machines. The Mamba model’s performance is then evaluated against established baseline models, including Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Amazon Chronos, to demonstrate its potential for accurate prediction of CPU utilisation. The MambaNet0 model achieved a 29% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) compared to the best-performing baseline Amazon Chronos. These findings reinforce the Mamba model’s ability to forecast accurate CPU utilisation, highlighting its potential for optimising cloud resource allocation in contribution to net zero goals. Full article

Enhancing interlayer bond strength remains a critical challenge in the extrusion-based 3D printing of cementitious materials. This study investigates the optimisation of interlayer bond strength in extrusion-based 3D-printed cementitious materials through a combined application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Using a concise yet comprehensive dataset, RSM provided interpretable main effects, curvature, and interactions, while the ANN captured non-linearities beyond quadratic forms. Comparative analysis revealed that the RSM model achieved higher predictive accuracy ($R^2=0.95$) compared to the ANN model ($R^2=0.87$). Desirability-based optimisation confirmed the critical importance of minimising casting delays to mitigate interlayer weaknesses, with RSM suggesting a water-to-cement (W/C) ratio of approximately 0.45 and a minimal time gap of less than 5 min, while ANN predicted slightly lower optimal W/C values but with reduced reliability due to the limited dataset. Sensitivity analysis using partial dependence plots (PDPs) further highlighted that ordinary Portland cement (OPC) content and W/C ratio are the dominant factors, contributing approximately 2.0 and 1.8 MPa respectively to the variation in predicted bond strength, followed by superplasticiser dosage and silica content. Variables such as water content, viscosity-modifying agent, and time gap exhibited moderate influence, while sand and fibre content had marginal effects within the tested ranges. These results demonstrate that RSM provides robust predictive performance and interpretable optimisation guidance, while ANN offers flexible non-linear modelling but requires larger datasets to achieve stable generalisation. Integrating both methods offers a complementary pathway to advance mix design and process control strategies in 3D concrete printing. Full article

A biomimetic adaptive façade applies natural principles to building design using shading devices that dynamically respond to environmental changes, enhancing daylight, thermal comfort, and energy efficiency. While motorised systems offer precision through sensors and mechanical actuation, they consume energy and are complex. In contrast, passively actuated systems use smart materials that respond to environmental stimuli, offering simpler and more sustainable operation, but often lack responsiveness to dynamic conditions. This study explores a sequential approach by initially developing motorised shading concepts before transitioning to a passive actuation strategy. In the first phase, nine mechanically actuated shading device concepts were designed, inspired by the opening and closing behaviour of plant stomata, and evaluated on structural robustness, actuation efficiency, ease of installation, and visual integration. One concept was selected for further development. In the second phase, a biocomposite made of polylactic acid (PLA) and regenerated cellulose fibres was used for Fused Deposition Modelling (FDM) to fabricate 3D-printed modules with passive, moisture-responsive actuation. The modules underwent environmental testing, demonstrating repeatable shape changes in response to heat and moisture. Moisture application increased the range of motion, and heating led to flap closure as water evaporated. Reinforcement and layering strategies were also explored to optimise movement and minimise unwanted deformation, highlighting the material’s potential for sustainable, responsive façade systems. Full article